Biomarker identification

ABSTRACT

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

RELATED APPLICATIONS

This application is a U.S. national phase application of International Application No. PCT/AU2014/050075 entitled “Biomarker Identification,” filed on 18 Jun. 2014, which claims priority to Australian Provisional Application No. 2013902243 entitled “Biomarker Identification,” filed on 20 Jun. 2013, the subject matter of each of which is hereby incorporated herein by reference in its entirety.

STATEMENT REGARDING SEQUENCE LISTING

The Sequence Listing associated with this application is provided in text format in lieu of a paper copy, and is hereby incorporated by reference into the specification. The name of the text file containing the Sequence Listing is DAVI_039_01US_ST25.txt. The text file is 2,527 KB, was created on Apr. 18, 2016, and is being submitted electronically via EFS-Web.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS (inSIRS) or infection-positive SIRS (ipSIRS).

DESCRIPTION OF THE PRIOR ART

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgement or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

The analysis of gene expression products for diagnostic purposes is known. Such analysis requires identification of one or more genes that can be used to generate a signature for use in distinguishing between different conditions. However, such identification can require the analysis of many gene expression products, which can be mathematically complex, computationally expensive and hence difficult. Much of the biomarker discovery process is devoted to identifying a subset of the data that may have relevant import, from which a signature is derived using a combination of these values to produce a model for diagnostic or prognostic use.

WO2004044236 describes a method of determining the status of a subject. In particular, this is achieved by obtaining subject data including respective values for each of a number of parameters, the parameter values being indicative of the current biological status of the subject. The subject data are compared to predetermined data that includes values for at least some of the parameters and an indication of the condition. The status of the subject, and in particular, the presence and/or absence of the one or more conditions, can then be determined in accordance with the results of the comparison.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides apparatus for identifying biomarkers, the apparatus including an electronic processing device that:

-   -   uses reference data from a plurality of individuals to define a         number of groups of individuals, the reference data including         measurements of the activity of a plurality of reference         biomarkers;     -   uses a plurality of analysis techniques to identify a number of         potential biomarkers from the plurality of reference biomarkers         that are potentially useful for distinguishing the groups of         individuals, allowing the potential biomarkers to be used in         generating signatures for use in clinical assessments.

Suitably, the electronic processing device, for each analysis technique:

-   -   using the analysis technique, identifies a number of reference         biomarkers that best distinguish the groups of individuals;     -   determines if the predictive performance of the identified         reference biomarkers exceeds a predetermined threshold; and,     -   in response to a successful determination, determines the         identified reference biomarkers to be potential biomarkers.

In some embodiments, the number of reference biomarkers is at least one of:

-   -   less than 10;     -   more than 1;     -   between 2 and 8; and,     -   5.

In some embodiments, the predetermined threshold is at least one of:

-   -   at least 90%;     -   at least 85%; and,     -   at least 80%.

Suitably, the electronic processing device:

-   -   adds potential biomarkers to a potential biomarker collection;         and,     -   removes the potential biomarkers from a reference biomarker         collection.

Suitably, for each of a plurality of analysis techniques the electronic processing device repeatedly identifies reference biomarkers as potential biomarkers until the predictive performance of the identified reference biomarkers falls below the predetermined threshold.

The electronic processing device may iteratively identify potential biomarkers.

In some embodiments, the electronic processing device uses a number of iterations including at least one of:

-   -   at least 100;     -   at least 500;     -   at least 1000;     -   at least 2000; and,     -   at least 5000.

The electronic processing device may repeatedly determine potential biomarkers until a predetermined number of potential biomarkers are identified.

Suitably, the predetermined number of potential biomarkers includes at least one of:

-   -   at least 100;     -   less than 500;     -   about 200.

In some embodiments, the analysis techniques include at least one of:

-   -   regression techniques;     -   correlation analysis; and,     -   a combination of regression and correlation techniques.

Suitably, the analysis techniques include:

-   -   sparse PLS;     -   random forest; and,     -   support vector machines.

In some embodiments, the electronic processing device:

-   -   removes a validation subgroup from the reference data prior to         determining the potential biomarkers;     -   determines the potential biomarkers using the reference data         without the validation subgroup; and,     -   uses the validation subgroup to validate at least one of:         -   the potential biomarkers; and,         -   signatures including a number of the potential biomarkers.

In some embodiments, the processing system determines the number of groups by classifying the individuals using at least one of:

-   -   an indication of a presence, absence, degree, or stage, or         progression of a condition;     -   phenotypic traits associated with the individuals;     -   genetic information associated with the individuals;     -   biomarkers associated with the individuals.

Suitably, the processing system determines groups at least in part using input commands from a user.

The reference data may include time series data indicative of the progression of a condition.

In some embodiments, the time series data is indicative of whether a condition that is at least one of:

-   -   improving;     -   worsening; and,     -   static.

The reference data may include for each of the individuals an indication of at least one of:

-   -   an activity of each of the reference biomarkers;     -   a degree of a condition;     -   a stage of a condition;     -   a presence of a condition;     -   an absence of a condition;     -   an indication of a condition progression;     -   phenotypic information;     -   genetic information; and,     -   a SOFA score.

In some embodiments, the electronic processing device identifies a number of potential biomarkers for use as signature biomarkers, the signature biomarkers being used in generating the signatures.

Suitably, the electronic processing device:

-   -   determines a clinical assessment; and,     -   identifies the signature biomarkers for the clinical assessment.

Suitably, the electronic processing device:

-   -   determines second groups of individuals relevant to the clinical         assessment;     -   using a second analysis technique, identifies a number of the         potential biomarkers that best distinguish the second groups of         individuals;     -   determines if the predictive performance of the identified         potential biomarkers exceeds a predetermined threshold; and,     -   in response to a successful determination, determines the         identified potential biomarkers to be signature biomarkers.

In some embodiments, the electronic processing device, in response to an unsuccessful determination:

-   -   modifies parameters of the second analysis technique; and,     -   uses the second analysis technique to identify alternative         potential biomarkers.

In some embodiments, the electronic processing device:

-   -   determines if the identified potential biomarkers are to be         excluded; and,     -   in response to a successful determination:     -   removes the potential biomarkers from a potential biomarker         database; and,     -   uses the second analysis technique to identify alternative         potential biomarkers for use as signature biomarkers.

Suitably, the second analysis technique includes at least one of:

-   -   ordinal regression and,     -   support vector machines.

In some embodiments, the signatures are indicative of:

-   -   activities of each of a number of signature biomarkers; and,     -   at least one of:     -   a SOFA score; and,     -   a presence, absence, degree, or stage, or progression of a         condition.

The signatures may be indicative of a presence, absence, degree, or stage or progression of at least one of:

-   -   infection-negative SIRS; and,     -   infection-positive SIRS.

In some embodiments, activities of at least some of the potential biomarkers are indicative of at least one of:

-   -   a presence, absence, degree, or stage, or progression of SIRS;     -   a healthy diagnosis;     -   a presence, absence, degree, or stage, or progression of         infection positive SIRS; and,     -   a presence, absence, degree, or stage, or progression of         infection negative SIRS.

Suitably, an activity of biomarkers are indicative of a level or abundance of a molecule selected from one or more of:

-   -   A nucleic acid molecule;     -   A proteinaceous molecule;     -   An amino acid     -   A carbohydrate;     -   A lipid;     -   A steroid;     -   An inorganic molecule;     -   An ion;     -   A drug;     -   A chemical;     -   A metabolite;     -   A toxin;     -   A nutrient;     -   A gas;     -   A cell;     -   A pathogenic organism; and,     -   A non pathogenic organism.

In another aspect, the present invention provides a method for determining the likelihood of the presence or absence of a condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and ipSIRS are absent), SIRS generally (i.e., not distinguishing between inSIRS or ipSIRS), inSIRS or ipSIRS, or to assess the likelihood of the presence, absence or risk of development of a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity), the method comprising: (1) correlating a reference Inflammatory Response Syndrome (IRS) biomarker profile with the presence or absence of a condition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein the reference IRS biomarker profile evaluates at least one IRS biomarker; (2) obtaining an IRS biomarker profile of a sample from a subject, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of: TLR5; CD177; VNN1; UBE2J1; IMP3; RNASE2//LOC643332; CLEC4D; C3AR1; GPR56; ARG1; FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2; GNLY; GALNT3; OMG; SLC37A3; BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3; CLEC4E; IL18R1; ACER3; ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53; PLB1; DSE; PTGDR; CAMK4; DNAJC13; TNFAIP6; FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5; MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2; B3GNT5//MCF2L2; GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C; C9orf72; HIST1H3I; SLC15A2; TLR10; ADM; CD274; CRIP1; LRRN3; HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP; MRPL41; SLC1A3; OLFM4; CASS4; TCN1; WSB2; CLU; ODZ1; KPNA5; PLAC8; CD63; HPSE; C1orf161; DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757; GOT2; PDGFC; B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IP011; TMEM144//LOC285505; CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH; PPP2R5A//SNORA16B; TMTC1; EAF2//HCG11//LOC647979; RCBTB2//LOC100131993; SEC24A//SAR1B; SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7; ANAPC11; EXOSC4; gene for IL-1beta-regulated neutrophil survival protein as set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2; RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163; FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2; EIF2AK2; LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth in GenBank Accession No. AK098012; EFCAB2; HIST1H2AA; HINT1; HIST1H3J; CDA; SAP30; AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2; GAB2; HS2ST1//UBA2; HIST1H3A; C22orf37; HLA-DPA1; VOPP1//LOC100128019; SLC39A8; MKI67; SLC11A1; AREG; ABCA1; DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2; CEACAM8; CLEC4A; PMS2CL//PMS2; REIN; PDE3B; SULF2; NEK6//LOC100129034; CENPK; TRAF3; GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP; CD24; GALNT2; HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A; ACTA2; ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL; EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4; MINPP1; TRIM21; ZNF28; NPCDR1; gene for protein FLJ21394 as set forth in GenBank Accession No. BC013935; gene for transcript set forth in GenBank Accession No. AK000992; ICAM1; TAF13; P4HA1//RPL17; C15orf54; KLHL5; HAL; DLEU2//DLEU2L; ANKRD28; LY6G5B//CSNK2B; KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746; HSPB1//HSPBL2; CCR4; TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM; PDK4; ABCG1; IL1B; THBS1; ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B; ZRANB1; TIMM10; FSD1L//GARNL1; HIST1H2AJ//HIST1H2AI; PTGS1; gene for transcript set forth in GenBank Accession No. BC008667; UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A; CCRL2; E2F6; MPZL3; SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2; S100B; ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1; FABP2; KDM6B//TMEM88; IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682; MYL9; HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2; MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript set forth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2; FAM118B; B4GALT3; RASA4//RASA4P//RASA4B//POLR2J4//LOC100132214; CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17; FBXL13; PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2; ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD; MPZL2; LOC100128751; IGJ; CDC26; PPP1R2//PPP1R2P3; IL5RA; ARL17P1//ARL17; ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D; CALM2//C2orf61; SPATA6; IGLV6-57; C1orf128; KRTAP15-1; IFI44; IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536; gene for transcript set forth in GenBank Accession No. BC034024; SDHC; NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1

In some embodiments, the method determines the likelihood that SIRS or a healthy condition is present or absent in the subject, and wherein the method comprises: 1) providing a correlation of a reference IRS biomarker profile with the presence or absence of SIRS or the healthy condition, wherein the reference biomarker profile evaluates at least one IRS biomarker selected from CD177, CLEC4D, BMX, VNN1, GPR84, ARG1, IL18R1, ERLIN1, IMP3, TLR5, UBE2J1, GPR56, FCGR1A, SLC1A3, SLC37A3, FAIM3, C3AR1, RNASE2, TNFAIP6, GNLY, OMG, FAR2, OLAH, CAMK4, METTL7B, B3GNT5, CLEC4E, MMP9, KREMEN1, GALNT3, PTGDR, TDRD9, GK3P, FKBP5, STOM, SMPDL3A, PFKFB2, ANKRD34B, SGMS2, DNAJC13, LRRN3, SH2D1B, C1orf161, HIST1H4C, IFI16, ACER3, PLB1, C9orf72, HMGB2, KLRK1, C7orf53, GOT2, TCN1, DSE, CCR3, CRIP1, ITK, KLRF1, TGFBR1, GSR, HIST1H4E, HPGD, FRMD3, ABCA13, C11orf82, PPP2R5A, BPI, CASS4, AP3B2, ODZ1, TMTC1, ADM, FGFBP2, HSPC159, HLA-DRA, HIST1H3I, TMEM144, MRPL41, FOLR3, PICALM, SH3PXD2B, DDAH2, HLA-DPB1, KPNA5, PHOSPHO1, TPST1, EIF2AK2, OR9A2, OLFM4, CD163, CDA, CHI3L1, MTHFS, CLU, ANAPC11, JUP, PMAIP1, GIMAP7, KLRD1, CCR1, CD274, EFCAB2, SUCNR1, KCNMA1, LGALS2, SLC11A1, FOXD4L3, VAMP2, ITGA4, LHFP, PRR13, FFAR2, B3GAT3, EAF2, HPSE, CLC, TLR10, CCR4, HIST1H3A, CENPK, DPH3, HLA-DPA1, ATP13A3, DNAJC9, S100B, HIST1H3J, 110, RPL17, C15orf54, LRRC70, IL5RA, PLA2G7, ECHDC3, HINT1, LCN2, PPIF, SLC15A2, PMS2CL, HIST1H2AA, CEACAM8, HSP90AB1, ABCG1, PDGFC, NPCDR1, PDK4, GAB2, WSB2, FAM118A, JKAMP, TREML1, PYHIN1, IRF4, ABCA1, DAAM2, ACPL2, RCBTB2, SAP30, THBS1, PCOLCE2, GPR65, NF-E4, LTF, LASS4, B4GALT3, RETN, TIMM10, IL1B, CLEC4A, SEC24A, RUNX2, LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2, EXOSC4, CCDC125, LOC284757, ANKRD28, HIST1H2AJ, CD63, PLIN2, SON, HIST1H4L, KRTAP15-1, DLEU2, MYL9, FABP2, CD24, MACF1, GSTO1, RRP12, AIG1, RASA4, FBXL13, PDE3B, CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6, CYP4F3, 199, TAAR1, TSHZ2, PLB1, UBE2F; (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker, and (3) determining a likelihood of the subject having or not having the healthy condition or SIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

Suitably, the method determines the likelihood that inSIRS, ipSIRS or a healthy condition is present or absent in the subject, wherein the method comprises: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having inSIRS, ipSIRS or the healthy condition, wherein the reference biomarker profile evaluates at least one IRS biomarker selected from PLAC8, 132, INSIG1, CDS2, VOPP1, SLC39A9, B3GAT3, CD300A, OCR1, PTGER2, LGALS1, HIST1H4L, AMFR, SIAE, SLC39A8, TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3, KIAA1257, OMG, HIST1H2BM, TDRD9, C22orf37, GALNT3, SYNE2, MGST3, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, HSPB1, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, KREMEN1, IRS2, APOLD1, RBP7, DNAJC13, ERGIC1, FSD1L, TLR5, TMEM62, SDHC, C9orf72, NP, KIAA0746, PMAIP1, DSE, SMPDL3A, DNAJC9, HIST1H3H, CDC26, CRIP1, FAR2, FRMD3, RGS2, METTL7B, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, IRF1, C7orf53, IGK@, RNASE2, FCGR1A, SAP30, PMS2CL, SLC11A1, AREG, PLB1, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125, GPR56, PPP1R2, 110, ACPL2, HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J, KLRD1, GPR84, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, HSP90AB1, HPGD, IFI16, CD177, TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3, HIST1H2AA, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having inSIRS, ipSIRS or a healthy condition the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In some embodiments, the method determines the likelihood that inSIRS or ipSIRS is present or absent in the subject, wherein the method comprises: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having inSIRS or ipSIRS, wherein the reference biomarker profile evaluates at least one IRS biomarker selected from C11orf82, PLAC8, 132, INSIG1, CDS2, VOPP1, SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10, PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2, SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41, TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1, NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM, MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2, HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1, KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1, FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1, KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDC26, ICAM1, LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B, C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL, SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7, GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125, SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ, FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO, TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B, IFI16, CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3, ANKRD28, TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having inSIRS or ipSIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

Suitably, the method determines the likelihood that a stage of ipSIRS selected from mild sepsis, severe sepsis and septic shock is present or absent the subject, wherein the method comprises: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having the stage of ipSIRS, wherein the reference biomarker IRS biomarker profile evaluates at least one IRS biomarker selected from PLEKHA3, PLEKHF2, 232, SFRS9, ZNF587, KPNA5, LOC284757, GPR65, VAMP2, SLC1A3, ITK, ATF7, ZNF28, AIF1, MINPP1, GIMAP7, MKI67, IRF4, TSHZ2, HLA-DPB1, EFCAB2, POLE2, FAIM3, 110, CAMK4, TRIM21, IFI44, CENPK, ATP5L, GPR56, HLA-DPA1, C4orf3, GSR, GNLY, RFESD, BPI, HIST1H2AA, NF-E4, CALM2, EIF1AX, E2F6, ARL17P1, TLR5, SH3PXD2B, FAM118A, RETN, PMAIP1, DNAJC9, PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2, JKAMP, JUP, ABCG1, SLC39A9, B3GNT5, ACER3, LRRC70, NPCDR1, TYMS, HLA-DRA, TDRD9, FSD1L, FAR2, C7orf53, PPP1R2, SGMS2, EXOSC4, TGFBR1, CD24, TCN1, TAF13, AP3B2, CD63, SLC15A2, IL18R1, ATP6V0D1, SON, HSP90AB1, CEACAM8, SMPDL3A, IMP3, SEC24A, PICALM, 199, CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1, KIAA0746, KIAA1324, HINT1, VNN1, C22orf37, FSD1L, FOLR3, IL1RL1, OMG, MTHFS, OLFM4, S100B, ITGA4, KLRD1, SLC39A8, KLHL5, KLRK1, MPO, PPIF, GOT2, LRRN3, HIST1H2AJ, CLU, LCN2, 132, CEP97, KLRF1, FBXL13, HIST1H3B, ANKRD34B, RPIA, HPGD, HIST2H2BF, GK3P (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having the stage of ipSIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In illustrative examples, an individual IRS biomarker is selected from the group consisting of: (a) a polynucleotide expression product comprising a nucleotide sequence that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence identity with the sequence set forth in any one of SEQ ID NO: 1-319, or a complement thereof; (b) a polynucleotide expression product comprising a nucleotide sequence that encodes a polypeptide comprising the amino acid sequence set forth in any one of SEQ ID NO: 320-619; (c) a polynucleotide expression product comprising a nucleotide sequence that encodes a polypeptide that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence similarity or identity with at least a portion of the sequence set forth in SEQ ID NO: 320-619; (d) a polynucleotide expression product comprising a nucleotide sequence that hybridizes to the sequence of (a), (b), (c) or a complement thereof, under medium or high stringency conditions; (e) a polypeptide expression product comprising the amino acid sequence set forth in any one of SEQ ID NO: 320-619; and (f) a polypeptide expression product comprising an amino acid sequence that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence similarity or identity with the sequence set forth in any one of SEQ ID NO: 320-619.

Evaluation of IRS markers suitably includes determining the levels of individual IRS markers, which correlate with the presence or absence of a condition, as defined above.

In some embodiments, the method of determining the likelihood of the presence or absence of a condition, as broadly described above, comprises comparing the level of a first IRS biomarker in the sample IRS biomarker profile with the level of a second IRS biomarker in the sample IRS biomarker profile to provide a ratio and determining a likelihood of the presence or absence of the condition based on that ratio. In illustrative examples of this type, the determination is carried out in the absence of comparing the level of the first or second IRS biomarkers in the sample IRS biomarker profile to the level of a corresponding IRS biomarker in the reference IRS biomarker profile. Representative IRS biomarkers that are useful for these embodiments are suitably selected from those listed in Example 6 and Tables 16-21.

In a related aspect, the present invention provides a kit comprising one or more reagents and/or devices for use in performing the method of determining the likelihood of the presence or absence of a condition as broadly described above.

Another aspect of the present invention provides a method for treating, preventing or inhibiting the development of inSIRS, ipSIRS or a particular stage of ipSIRS in a subject, the method comprising: (1) correlating a reference IRS biomarker profile with the presence or absence of a condition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein the reference IRS biomarker profile evaluates at least one IRS biomarker; (2) obtaining an IRS biomarker profile of a sample from a subject, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; (3) determining a likelihood of the subject having or not having the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile, and administering to the subject, on the basis that the subject has an increased likelihood of having inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the subject has an increased likelihood of having ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.

Yet another aspect of the present invention provides a method of monitoring the efficacy of a particular treatment regimen in a subject towards a desired health state (e.g., healthy condition), the method comprising: (1) providing a correlation of a reference IRS biomarker profile with the likelihood of having a healthy condition; (2) obtaining a corresponding IRS biomarker profile of a subject having inSIRS, ipSIRS or a particular stage of ipSIRS after treatment with a treatment regimen, wherein a similarity of the subject's IRS biomarker profile after treatment to the reference IRS biomarker profile indicates the likelihood that the treatment regimen is effective for changing the health status of the subject to the desired health state.

Still another aspect of the present invention provides a method of correlating a reference IRS biomarker profile with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS, wherein the reference IRS biomarker profile evaluates at least one IRS biomarker, the method comprising: (a) determining a sample IRS biomarker profile from a subject with the condition prior to treatment, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and correlating the sample IRS biomarker profile with a treatment regimen that is effective for treating the condition.

In another aspect, the present invention provides a method of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS, the method comprising: (a) correlating a reference biomarker profile prior to treatment with an effective treatment regimen for the condition, wherein the reference IRS biomarker profile evaluates at least one IRS biomarker; and (b) obtaining a sample IRS biomarker profile from the subject after treatment, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker, and wherein the sample IRS biomarker profile after treatment indicates whether the treatment regimen is effective for treating the condition in the subject.

In a further aspect, the present invention provides a method of correlating an IRS biomarker profile with a positive or negative response to a treatment regimen, the method comprising: (a) obtaining an IRS biomarker profile from a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS following commencement of the treatment regimen, wherein the IRS biomarker profile evaluates at least one IRS biomarker; and (b) correlating the IRS biomarker profile from the subject with a positive or negative response to the treatment regimen.

Another aspect of the present invention provides a method of determining a positive or negative response to a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS, the method comprising: (a) correlating a reference IRS biomarker profile with a positive or negative response to the treatment regimen, wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) determining a sample IRS biomarker profile from the subject, wherein the subject's sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker and indicates whether the subject is responding to the treatment regimen.

In some embodiments, the method of determining a positive or negative response to a treatment regimen further comprises: determining a first sample IRS biomarker profile from the subject prior to commencing the treatment regimen, wherein the first sample IRS biomarker profile evaluates at least one IRS biomarker; and comparing the first sample IRS biomarker profile with a second sample IRS biomarker profile from the subject after commencement of the treatment regimen, wherein the second sample IRS biomarker profile evaluates for an individual IRS biomarker in the first sample IRS biomarker profile a corresponding IRS biomarker.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a box and whiskers plot of PLEKHA3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 2 shows a box and whiskers plot of VAMP2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 3 shows a box and whiskers plot of ITK gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 4 shows a box and whiskers plot of C11orf82 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 5 shows a box and whiskers plot of PLAC8 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 6 shows a box and whiskers plot of INSIG1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 7 shows a box and whiskers plot of FCGR1A//FCGR1B//FCGR1C gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 8 shows a box and whiskers plot of CHI3L1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 9 shows a box and whiskers plot of CD177 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 10 shows a box and whiskers plot of GNLY gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 11 shows a box and whiskers plot of BMX//HNRPDL gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 12 shows a box and whiskers plot of TLR5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 13 shows a box and whiskers plot of TLR5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 14 shows a box and whiskers plot of CD177 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 15 shows a box and whiskers plot of VNN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 16 shows a box and whiskers plot of UBE2J1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 17 shows a box and whiskers plot of IMP3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 18 shows a box and whiskers plot of RNASE2//LOC643332 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 19 shows a box and whiskers plot of CLEC4D gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 20 shows a box and whiskers plot of C3AR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 21 shows a box and whiskers plot of GPR56 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 22 shows a box and whiskers plot of ARG1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 23 shows a box and whiskers plot of FCGR1A//FCGR1B//FCGR1C gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 24 shows a box and whiskers plot of C11orf82 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 25 shows a box and whiskers plot of FAR2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 26 shows a box and whiskers plot of GNLY gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 27 shows a box and whiskers plot of GALNT3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 28 shows a box and whiskers plot of OMG gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 29 shows a box and whiskers plot of SLC37A3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 30 shows a box and whiskers plot of BMX//HNRPDL gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 31 shows a box and whiskers plot of STOM gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 32 shows a box and whiskers plot of TDRD9 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 33 shows a box and whiskers plot of KREMEN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 34 shows a box and whiskers plot of FAIM3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 35 shows a box and whiskers plot of CLEC4E gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 36 shows a box and whiskers plot of IL18R1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 37 shows a box and whiskers plot of ACER3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 38 shows a box and whiskers plot of ERLIN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 39 shows a box and whiskers plot of TGFBR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 40 shows a box and whiskers plot of FKBP5//LOC285847 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 41 shows a box and whiskers plot of GPR84 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 42 shows a box and whiskers plot of C7orf53 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 43 shows a box and whiskers plot of PLB1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 44 shows a box and whiskers plot of DSE gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 45 shows a box and whiskers plot of PTGDR gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 46 shows a box and whiskers plot of CAMK4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 47 shows a box and whiskers plot of DNAJC13 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 48 shows a box and whiskers plot of TNFAIP6 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 49 shows a box and whiskers plot of FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 50 shows a box and whiskers plot of MMP9//LOC100128028 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 51 shows a box and whiskers plot of GSR gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 52 shows a box and whiskers plot of KLRF1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 53 shows a box and whiskers plot of SH2D1B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 54 shows a box and whiskers plot of ANKRD34B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 55 shows a box and whiskers plot of SGMS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 56 shows a box and whiskers plot of B3GNT5//MCF2L2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 57 shows a box and whiskers plot of GK3P//GK gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 58 shows a box and whiskers plot of PFKFB2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 59 shows a box and whiskers plot of PICALM gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 60 shows a box and whiskers plot of METTL7B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 61 shows a box and whiskers plot of HIST1H4C gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 62 shows a box and whiskers plot of C9orf72 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 63 shows a box and whiskers plot of HIST1H3I gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 64 shows a box and whiskers plot of SLC15A2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 65 shows a box and whiskers plot of TLR10 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 66 shows a box and whiskers plot of ADM gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 67 shows a box and whiskers plot of CD274 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 68 shows a box and whiskers plot of CRIP1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 69 shows a box and whiskers plot of LRRN3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 70 shows a box and whiskers plot of HLA-DPB1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 71 shows a box and whiskers plot of VAMP2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 72 shows a box and whiskers plot of SMPDL3A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 73 shows a box and whiskers plot of IFI16 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 74 shows a box and whiskers plot of JKAMP gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 75 shows a box and whiskers plot of MRPL41 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 76 shows a box and whiskers plot of SLC1A3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 77 shows a box and whiskers plot of OLFM4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 78 shows a box and whiskers plot of CASS4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 79 shows a box and whiskers plot of TCN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 80 shows a box and whiskers plot of WSB2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 81 shows a box and whiskers plot of CLU gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 82 shows a box and whiskers plot of ODZ1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 83 shows a box and whiskers plot of KPNA5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 84 shows a box and whiskers plot of PLAC8 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 85 shows a box and whiskers plot of CD63 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 86 shows a box and whiskers plot of HPSE gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 87 shows a box and whiskers plot of C1orf161 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 88 shows a box and whiskers plot of DDAH2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 89 shows a box and whiskers plot of KLRK1//KLRC4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 90 shows a box and whiskers plot of ATP13A3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 91 shows a box and whiskers plot of ITK gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 92 shows a box and whiskers plot of PMAIP1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 93 shows a box and whiskers plot of LOC284757 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 94 shows a box and whiskers plot of GOT2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 95 shows a box and whiskers plot of PDGFC gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 96 shows a box and whiskers plot of B3GAT3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 97 shows a box and whiskers plot of HIST1H4E gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 98 shows a box and whiskers plot of HPGD gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 99 shows a box and whiskers plot of FGFBP2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 100 shows a box and whiskers plot of LRRC70//IPO11 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 101 shows a box and whiskers plot of TMEM144/LOC285505 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 102 shows a box and whiskers plot of CDS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 103 shows a box and whiskers plot of BPI gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 104 shows a box and whiskers plot of ECHDC3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 105 shows a box and whiskers plot of CCR3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 106 shows a box and whiskers plot of HSPC159 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 107 shows a box and whiskers plot of OLAH gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 108 shows a box and whiskers plot of PPP2R5A//SNORA16B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 109 shows a box and whiskers plot of TMTC1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 110 shows a box and whiskers plot of EAF2//HCG11//LOC647979 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 111 shows a box and whiskers plot of RCBTB2//LOC100131993 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 112 shows a box and whiskers plot of SEC24A//SAR1B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 113 shows a box and whiskers plot of SH3PXD2B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 114 shows a box and whiskers plot of HMGB2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 115 shows a box and whiskers plot of KLRD1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 116 shows a box and whiskers plot of CHI3L1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 117 shows a box and whiskers plot of FRMD3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 118 shows a box and whiskers plot of SLC39A9 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 119 shows a box and whiskers plot of GIMAP7 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 120 shows a box and whiskers plot of ANAPC11 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 121 shows a box and whiskers plot of EXOSC4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 122 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 123 shows a box and whiskers plot of INSIG1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 124 shows a box and whiskers plot of FOLR3//FOLR2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 125 shows a box and whiskers plot of RUNX2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 126 shows a box and whiskers plot of PRR13//PCBP2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 127 shows a box and whiskers plot of HIST1H4L gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 128 shows a box and whiskers plot of LGALS1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 129 shows a box and whiskers plot of CCR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 130 shows a box and whiskers plot of TPST1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 131 shows a box and whiskers plot of HLA-DRA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 132 shows a box and whiskers plot of CD163 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 133 shows a box and whiskers plot of FFAR2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 134 shows a box and whiskers plot of PHOSPHO1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 135 shows a box and whiskers plot of PPIF gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 136 shows a box and whiskers plot of MTHFS gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 137 shows a box and whiskers plot of DNAJC9//FAM149B1//RPL26 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 138 shows a box and whiskers plot of LCN2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 139 shows a box and whiskers plot of EIF2AK2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 140 shows a box and whiskers plot of LGALS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 141 shows a box and whiskers plot of SIAE gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 142 shows a box and whiskers plot of AP3B2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 143 shows a box and whiskers plot of ABCA13 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 144 shows a box and whiskers plot of NA expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 145 shows a box and whiskers plot of EFCAB2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 146 shows a box and whiskers plot of HIST1H2AA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 147 shows a box and whiskers plot of HINT1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 148 shows a box and whiskers plot of HIST1H3J gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 149 shows a box and whiskers plot of CDA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 150 shows a box and whiskers plot of SAP30 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 151 shows a box and whiskers plot of AGTRAP gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 152 shows a box and whiskers plot of SUCNR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 153 shows a box and whiskers plot of MTRR gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 154 shows a box and whiskers plot of PLA2G7 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 155 shows a box and whiskers plot of AIG1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 156 shows a box and whiskers plot of PCOLCE2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 157 shows a box and whiskers plot of GAB2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 158 shows a box and whiskers plot of HS2ST1//UBA2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 159 shows a box and whiskers plot of HIST1H3A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 160 shows a box and whiskers plot of C22orf37 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 161 shows a box and whiskers plot of HLA-DPA1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 162 shows a box and whiskers plot of VOPP1//LOC100128019 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 163 shows a box and whiskers plot of SLC39A8 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 164 shows a box and whiskers plot of MKI67 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 165 shows a box and whiskers plot of SLC11A1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 166 shows a box and whiskers plot of AREG gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 167 shows a box and whiskers plot of ABCA1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 168 shows a box and whiskers plot of DAAM2//LOC100131657 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 169 shows a box and whiskers plot of LTF gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 170 shows a box and whiskers plot of TREML1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 171 shows a box and whiskers plot of GSTO1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 172 shows a box and whiskers plot of PTGER2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 173 shows a box and whiskers plot of CEACAM8 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 174 shows a box and whiskers plot of CLEC4A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 175 shows a box and whiskers plot of PMS2CL/PMS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 176 shows a box and whiskers plot of RETN gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 177 shows a box and whiskers plot of PDE3B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 178 shows a box and whiskers plot of SULF2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 179 shows a box and whiskers plot of NEK6//LOC100129034 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 180 shows a box and whiskers plot of CENPK gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 181 shows a box and whiskers plot of TRAF3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 182 shows a box and whiskers plot of GPR65 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 183 shows a box and whiskers plot of IRF4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 184 shows a box and whiskers plot of MACF1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 185 shows a box and whiskers plot of AMFR gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 186 shows a box and whiskers plot of RPL17//SNORD58B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 187 shows a box and whiskers plot of IRS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 188 shows a box and whiskers plot of JUP gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 189 shows a box and whiskers plot of CD24 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 190 shows a box and whiskers plot of GALNT2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 191 shows a box and whiskers plot of HSP90AB1//HSP90AB3P//HSP90AB2P gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 192 shows a box and whiskers plot of GLT25D1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 193 shows a box and whiskers plot of OR9A2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 194 shows a box and whiskers plot of HDHD1A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 195 shows a box and whiskers plot of ACTA2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 196 shows a box and whiskers plot of ACPL2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 197 shows a box and whiskers plot of LRRFIP1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 198 shows a box and whiskers plot of KCNMA1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 199 shows a box and whiskers plot of OCR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 200 shows a box and whiskers plot of ITGA4//CERKL gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 201 shows a box and whiskers plot of EIF1AX//SCARNA9L//EIF1AP1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 202 shows a box and whiskers plot of SFRS9 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 203 shows a box and whiskers plot of DPH3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 204 shows a box and whiskers plot of ERGIC1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 205 shows a box and whiskers plot of CD300A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 206 shows a box and whiskers plot of NF-E4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 207 shows a box and whiskers plot of MINPP1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 208 shows a box and whiskers plot of TRIM21 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 209 shows a box and whiskers plot of ZNF28 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 210 shows a box and whiskers plot of NPCDR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 211 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 212 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 213 shows a box and whiskers plot of ICAM1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 214 shows a box and whiskers plot of TAF13 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 215 shows a box and whiskers plot of P4HA1//RPL17 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 216 shows a box and whiskers plot of C15orf54 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 217 shows a box and whiskers plot of KLHL5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 218 shows a box and whiskers plot of HAL gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 219 shows a box and whiskers plot of DLEU2//DLEU2L gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 220 shows a box and whiskers plot of ANKRD28 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 221 shows a box and whiskers plot of LY6G5B//CSNK2B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 222 shows a box and whiskers plot of KIAA1257//ACAD9//LOC100132731 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 223 shows a box and whiskers plot of MGST3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 224 shows a box and whiskers plot of KIAA0746 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 225 shows a box and whiskers plot of HSPB1//HSPBL2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 226 shows a box and whiskers plot of CCR4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 227 shows a box and whiskers plot of TYMS gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 228 shows a box and whiskers plot of RRP12//LOC644215 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 229 shows a box and whiskers plot of CCDC125 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 230 shows a box and whiskers plot of HIST1H2BM gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 231 shows a box and whiskers plot of PDK4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 232 shows a box and whiskers plot of ABCG1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 233 shows a box and whiskers plot of IL1B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 234 shows a box and whiskers plot of THBS1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 235 shows a box and whiskers plot of ITGA2B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 236 shows a box and whiskers plot of LHFP gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 237 shows a box and whiskers plot of LAIR1//LAIR2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 238 shows a box and whiskers plot of HIST1H3B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 239 shows a box and whiskers plot of ZRANB1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 240 shows a box and whiskers plot of TIMM10 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 241 shows a box and whiskers plot of FSD1L//GARNL1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 242 shows a box and whiskers plot of HIST1H2AJ//HIST1H2AI gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 243 shows a box and whiskers plot of PTGS1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 244 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 245 shows a box and whiskers plot of UBE2F//C20orf194//SCLY gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 246 shows a box and whiskers plot of HIST1H3C gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 247 shows a box and whiskers plot of FAM118A gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 248 shows a box and whiskers plot of CCRL2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 249 shows a box and whiskers plot of E2F6 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 250 shows a box and whiskers plot of MPZL3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 251 shows a box and whiskers plot of SRXN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 252 shows a box and whiskers plot of CD151 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 253 shows a box and whiskers plot of HIST1H3H gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 254 shows a box and whiskers plot of FSD1L gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 255 shows a box and whiskers plot of RFESD//SPATA9 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 256 shows a box and whiskers plot of TPX2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 257 shows a box and whiskers plot of S100B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 258 shows a box and whiskers plot of ZNF587//ZNF417 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 259 shows a box and whiskers plot of PYHIN1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 260 shows a box and whiskers plot of KIAA1324 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 261 shows a box and whiskers plot of CEACAM6//CEACAM5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 262 shows a box and whiskers plot of APOLD1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 263 shows a box and whiskers plot of FABP2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 264 shows a box and whiskers plot of KDM6B//TMEM88 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 265 shows a box and whiskers plot of IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 266 shows a box and whiskers plot of MYL9 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 267 shows a box and whiskers plot of HIST1H2BJ gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 268 shows a box and whiskers plot of TAAR1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 269 shows a box and whiskers plot of CLC gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 270 shows a box and whiskers plot of CYP4F3//CYP4F2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 271 shows a box and whiskers plot of CEP97 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 272 shows a box and whiskers plot of SON gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 273 shows a box and whiskers plot of IRF1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 274 shows a box and whiskers plot of SYNE2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 275 shows a box and whiskers plot of MME gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 276 shows a box and whiskers plot of LASS4 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 277 shows a box and whiskers plot of DEFA4//DEFA8P gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 278 shows a box and whiskers plot of C7orf58 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 279 shows a box and whiskers plot of DYNLL1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 280 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 281 shows a box and whiskers plot of MPO gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 282 shows a box and whiskers plot of CPM gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 283 shows a box and whiskers plot of TSHZ2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 284 shows a box and whiskers plot of PLIN2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 285 shows a box and whiskers plot of FAM118B gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 286 shows a box and whiskers plot of B4GALT3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 287 shows a box and whiskers plot of RASA4//RASA4P//RASA4B//POLR2J4//LOC100132214 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 288 shows a box and whiskers plot of CTSL1//CTSLL3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 289 shows a box and whiskers plot of NP gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 290 shows a box and whiskers plot of ATF7 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 291 shows a box and whiskers plot of SPARC gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 292 shows a box and whiskers plot of PLB1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 293 shows a box and whiskers plot of C4orf3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 294 shows a box and whiskers plot of POLE2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 295 shows a box and whiskers plot of TNFRSF17 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 296 shows a box and whiskers plot of FBXL13 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 297 shows a box and whiskers plot of PLEKHA3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 298 shows a box and whiskers plot of TMEM62//SPCS2//LOC653566 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 299 shows a box and whiskers plot of RBP7 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 300 shows a box and whiskers plot of PLEKHF2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 301 shows a box and whiskers plot of RGS2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 302 shows a box and whiskers plot of ATP6V0D1//LOC100132855 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 303 shows a box and whiskers plot of RPIA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 304 shows a box and whiskers plot of CAMK1D gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 305 shows a box and whiskers plot of IL1RL1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 306 shows a box and whiskers plot of CMTM5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 307 shows a box and whiskers plot of AIF1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 308 shows a box and whiskers plot of CFD gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 309 shows a box and whiskers plot of MPZL2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 310 shows a box and whiskers plot of LOC100128751 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 311 shows a box and whiskers plot of IGJ gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 312 shows a box and whiskers plot of CDC26 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 313 shows a box and whiskers plot of PPP1R2//PPP1R2P3 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 314 shows a box and whiskers plot of IL5RA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 315 shows a box and whiskers plot of ARL17P1//ARL17 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 316 shows a box and whiskers plot of ATP5L//ATP5L2 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 317 shows a box and whiskers plot of TAS2R31 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 318 shows a box and whiskers plot of HIST2H2BF//HIST2H3D gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 319 shows a box and whiskers plot of CALM2//C2orf61 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 320 shows a box and whiskers plot of SPATA6 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 321 shows a box and whiskers plot of IGLV6-57 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 322 shows a box and whiskers plot of C1orf128 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 323 shows a box and whiskers plot of KRTAP15-1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 324 shows a box and whiskers plot of IFI44 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 325 shows a box and whiskers plot of IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 326 shows a box and whiskers plot of NA gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 327 shows a box and whiskers plot of SDHC gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 328 shows a box and whiskers plot of NFXL1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 329 shows a box and whiskers plot of GLDC gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 330 shows a box and whiskers plot of DCTN5 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 331 shows a box and whiskers plot of KIAA0101//CSNK1G1 gene expression for healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An example process for performing biomarker identification will now be described. For the purpose of this example, it is assumed that the process is performed at least in part using an electronic processing device, such as a processor of a computer system, as will be described in more detail below.

Furthermore, for the purpose of explanation, different terms will be used to identify biomarkers at different stages of the process. For example, the term “reference biomarkers” is used to refer to biomarkers whose activity has been quantified for a sample population of reference individuals having different conditions, stages of different conditions, subtypes of different conditions or with different prognoses. The different reference biomarkers measured for the individuals may be referred to as a reference biomarker collection. The term “reference data” refers to data measured for the individuals in the sample population, and may include quantification of the activity of the biomarkers measured for each individual, information regarding any conditions of the individuals, and optionally any other information of interest. The number of reference biomarkers will vary, but is typically more than 1000 biomarkers.

The term “potential biomarkers” refers to a subset of the reference biomarkers that have been identified as being potentially useful in distinguishing between different groups of individuals, such as individuals suffering from different conditions, or having different stages or prognoses. The number of potential biomarkers will vary, but is typically about 200. The different potential biomarkers may be referred to as a potential biomarker collection.

The term “remaining reference biomarkers” refers to reference biomarkers remaining in the reference biomarker collection, once potential biomarkers have been removed.

The term “signature biomarkers” is used to refer to a subset of the potential biomarkers that have been identified as being potentially useful in defining signatures that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of signature biomarkers will vary, but is typically of the order of 10 or less, with the different signature biomarkers identified being referred to as a signature biomarker collection.

It will be appreciated that the above described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.

In this example, at step 100, the process involves using reference data from a plurality of individuals to define a number of groups of individuals. The individuals are taken from a reference population, typically including individuals having a range of different conditions, or stages of different conditions, or subtypes of different conditions or with different prognoses.

The reference data typically includes measurements of a plurality of reference biomarkers, the measurements including information regarding the activity, such as the level or abundance, of any expression product or measurable molecule, as will be described in more detail below. The reference data may also include other additional relevant information such as clinical data regarding one or more conditions suffered by each individual. This can include information regarding a presence, absence, degree, stage, severity or progression of a condition, phenotypic information, such as details of phenotypic traits, genetic or genetically regulated information, amino acid or nucleotide related genomics information, results of other tests including imaging, biochemical and hematological assays, other physiological scores such as a SOFA (Sequential Organ Failure Assessment) score, or the like and this is not intended to be limiting, as will be apparent from the description below.

At step 110, a plurality of analysis techniques, such as statistical analysis or machine learning techniques, are used to identify a number of potential biomarkers from the plurality of reference biomarkers that are potentially useful for distinguishing the groups of individuals, allowing the potential biomarkers to be used in selecting signature biomarkers for use in generating signatures for use in clinical assessments.

The analysis techniques are typically applied in an iterative fashion, with each iteration being used to identify a subset of reference biomarkers that might prove suitable for use as potential biomarkers. In one example, as each iteration is performed, the predictive performance of the reference biomarkers in distinguishing the groups is assessed, with reference biomarkers being identified for use as potential biomarkers only in the event that they exceed a predetermined predictive performance threshold, such as at least 90%, at least 85% or more typically, at least 80%. This threshold may be implemented as accuracy in the case of classification or a measure of correlation in the case of continuous outcomes.

Once reference biomarkers are identified for use as potential biomarkers, they can be removed from the reference biomarker collection, allowing the next iteration to be performed on the remaining reference biomarkers. The number of iterations will depend on the analysis techniques and associated parameters used, and can include at least 100, at least 500, at least 1000, at least 2000 and even at least 5000.

The process uses a plurality of different analysis techniques, such as classification, regression and/or machine learning techniques, allowing a variety of potential biomarkers to be identified. This is performed as each analysis technique typically operates slightly differently and as a result will often identify different potential biomarkers, so using the plurality of different analysis techniques ensures that as many potentially useful biomarkers as possible are captured for use as potential biomarkers.

The analysis techniques may be performed until the predictive performance of the remaining reference biomarkers in the reference biomarker collection falls below the predetermined threshold and each technique has been used, or may be repeated until a predetermined number of potential biomarkers, such as at least 100, less than 500 or more typically about 200, are identified.

Following identification of potential biomarkers, at step 120, a subset of the potential biomarkers can be optionally identified for use as signature biomarkers, to allow signatures for use in specific clinical assessments to be determined. This can be achieved in any suitable manner, but in one example, this involves a further process of identifying specific groups relevant to the clinical assessment, and then performing a further regression or other similar statistical analysis to select those potential biomarkers that can be used as signature biomarkers.

Accordingly, in one example, the above described process is used to identify a subset of measured reference biomarkers that can act as potential biomarkers, before a more in depth analysis is performed to identify a subset of potential biomarkers for use as signature biomarkers that can be used in specific clinical assessments. As a result, the above process can act as a coarse filter, allowing a relatively large number of potential biomarkers to be identified that can be used in distinguishing the different groups of individuals.

By way of example, many patients suffer from a condition called Systemic Inflammatory Response Syndrome (SIRS) (M S Rangel-Frausto, D Pittet, M Costigan, T Hwang, C S Davis, and R P Wenzel, “The Natural History of the Systemic Inflammatory Response Syndrome (SIRS). a Prospective Study.,” JAMA: the Journal of the American Medical Association 273, no. 2 (Jan. 11, 1995): 117-123.). SIRS is an overwhelming whole body reaction that may have an infectious or non-infectious aetiology, whereas sepsis is SIRS that occurs during infection. Both are defined by a number of non-specific host response parameters including changes in heart and respiratory rate, body temperature and white cell counts (Mitchell M Levy et al., “2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference,” Critical Care Medicine 31, no. 4 (April 2003): 1250-1256, doi:10.1097/01.CCM.0000050454.01978.3B.; K Reinhart, M Bauer, N C Riedemann, and C S Hartog, “New Approaches to Sepsis: Molecular Diagnostics and Biomarkers,” Clinical Microbiology Reviews 25, no. 4 (Oct. 3, 2012): 609-634, doi:10.1128/CMR.00016-12.) To differentiate these conditions they are referred herein to as SIRS (both conditions), infection-negative SIRS (SIRS without infection, hereafter referred to as “inSIRS”) and infection-positive SIRS (sepsis, SIRS with a known or suspected infection, hereafter referred to as “ipSIRS”). The causes of SIRS are multiple and varied and can include, but are not limited to, trauma, burns, pancreatitis, endotoxemia, surgery, adverse drug reactions, and infections (local and systemic). Using two patient populations of healthy individuals and individuals having SIRS, a coarse filter can be used to identify which reference biomarkers can distinguish these two groups of individuals, thereby allowing potential biomarkers to be identified. A coarse filter could also be used to identify which reference biomarkers can separate inSIRS patients from ipSIRS patients, both groups of patients having SIRS in common, but each group of patients differing in whether a clinician has determined the presence of an infection or not.

Following this, more specific and computationally intensive analyses could be performed to identify a subset of potential biomarkers for use as signature biomarkers to answer more specific clinical questions such as: for patients with ipSIRS which biomarkers can separate out those with severe sepsis or septic shock, or provide a prognosis or indication of likely progression to another stage of disease, or for patients with inSIRS which biomarkers can separate those with pancreatitis from those following surgery.

Thus, if it is desired to make clinical assessments relating to SIRS, and in particular, inSIRS and ipSIRS, a suite of biomarkers can be quantified for individuals suffering either one of these conditions, as well as healthy individuals and used as reference biomarkers. These data can be used to define first groups of individuals having one of the two conditions or both, as well as of healthy individuals. Potential biomarkers can be ascertained that may be used to distinguish these groups. For example, the first stage could be used to determine biomarkers that differentiate healthy individuals and individuals having SIRS.

Following this, signature biomarkers for specific clinical assessments within these groups, such as distinguishing inSIRS from ipSIRS (rule in and rule out ipSIRS), can be determined. In this case, second groups are defined that relate to individuals having or not having infection-positive or inSIRS, and then signature biomarkers are determined from the potential biomarkers.

It can be complex and computationally difficult to select a limited number of clinically useful and manageable biomarkers from a large data set in a single stage. Thus, using a single stage identification process, potentially useful biomarkers can be easily overlooked or omitted, so that the resulting signature biomarkers are not necessarily the best suited for use in a specific clinical assessment. A particular benefit of the described approach is that by separating the process into multiple stages, the chances of overlooking or omitting the discovery of new and clinically useful biomarkers is greatly reduced.

The multi-stage approach allows coarse filtering to be used first so as to limit the number of measured reference biomarkers to a more manageable number of potential biomarkers, so that more specific, and computationally intensive, techniques can be used to identify signature biomarkers for use in specific clinical assessments. The coarse analysis therefore allows a collection of potential biomarkers to be established that will be relevant to a range of different but related clinical assessments. A more focussed analysis can then be performed to identify specific signature biomarkers, which is less computationally intensive than attempting to do this for a greater number of biomarkers, and also helps ensure the best biomarkers for the clinical assessment are identified by excluding the noise introduced by many uninformative biomarkers which have been removed from consideration.

The above approach can therefore allow a large number of measured reference biomarkers, typically several thousand, to be used as a basis for the analysis, thereby reducing the likelihood of new and clinically relevant biomarkers being excluded from the resulting potential biomarkers, and ultimately signature biomarkers, hence improving the ability of the signatures to be clinically useful in assessments.

In one example, the process is performed at least in part using a processing system, such as a suitably programmed computer system. This can be performed on a stand-alone computer, with the microprocessor executing applications software allowing the above-described method to be performed. Alternatively, the process can be performed by one or more processing systems operating as part of a distributed architecture, an example of which will now be described.

In this example, a base station 201 is coupled via a communications network, such as the Internet 202, and/or a number of local area networks (LANs) 204, to a number of computer systems 203. It will be appreciated that the configuration of the networks 202, 204 are for the purpose of example only, and in practice the base station 201, computer systems 203 can communicate via any appropriate mechanism, such as via wired or wireless connections, including, but not limited to mobile networks, private networks, such as an 802.11 networks, the Internet, LANs, WANs, or the like, as well as via direct or point-to-point connections, such as Bluetooth, or the like.

In one example, the base station 201 includes a processing system 210 coupled to a database 211. The base station 201 is adapted to be used in analysing reference data, selecting potential biomarkers, and optionally generating signatures for use in clinical assessments. The reference data may be stored in the database 211 and may be received from the computer systems 203, or other remote devices. The base station 201 may also be adapted to assist in performing clinical assessments by comparing individual data relating to a patient or other individual and then comparing this to the signatures to allow a clinical assessment to be made. The computer systems 203 are therefore adapted to communicate with the base station 201, allowing data to be transferred there between and/or to control the operation of the base station 201.

Whilst the base station 201 is a shown as a single entity, it will be appreciated that the base station 201 can be distributed over a number of geographically separate locations, for example by using processing systems 210 and/or databases 211 that are provided as part of a cloud based environment.

However, the above-described arrangement is not essential and other suitable configurations could be used. For example, the process for identifying biomarkers, as well as any subsequent clinical assessment of individual data could be performed on a stand-alone computer system.

An example of a suitable processing system 210 includes at least one microprocessor 300, a memory 301, an input/output device 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example the external interface 303 can be utilised for connecting the processing system 210 to peripheral devices, such as the communications networks 202, 204, databases 211, other storage devices, or the like. Although a single external interface 303 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 300 executes instructions in the form of applications software stored in the memory 301 to allow the biomarker identification process to be performed, as well as to perform any other required processes, such as communicating with the computer systems 203. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

Accordingly, it will be appreciated that the processing system 300 may be formed from any suitable processing system, such as a suitably programmed computer system, PC, web server, network server, or the like. In one particular example, the processing system 100 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing system could be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

In one example, the computer system 203 includes at least one microprocessor 400, a memory 401, an input/output device 402, such as a keyboard and/or display, and an external interface 403, interconnected via a bus 404 as shown. In this example the external interface 403 can be utilised for connecting the computer system 203 to peripheral devices, such as the communications networks 202, 204, databases 211, other storage devices, or the like. Although a single external interface 403 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (eg. Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 400 executes instructions in the form of applications software stored in the memory 401 to allow communication with the base station 201, for example to allow data to be supplied thereto and allowing results of any clinical assessment to be displayed to an operator. The computer system 203 may also be used to allow the operation of the base station 201 to be controlled, for example to allow the biomarker identification process to be performed remotely.

Accordingly, it will be appreciated that the computer systems 203 may be formed from any suitable processing system, such as a suitably programmed PC, Internet terminal, lap-top, hand-held PC, smart phone, PDA, web server, or the like. Thus, in one example, the processing system 100 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the computer systems 203 can be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

Examples of the biomarker identification process, and subsequent use in a clinical assessment will now be described in further detail. For the purpose of these examples, it is assumed that reference data, including the reference biomarker collection, any potential biomarkers, signature biomarkers or signatures can be stored in the database 211, and that the biomarker identification process is performed using the processing system 210 under control of one of the computer systems 203. Thus, it is assumed that the processing system 210 of the base station 201 hosts applications software for performing the biomarker identification process, with actions performed by the processing system 210 being performed by the processor 300 in accordance with instructions stored as applications software in the memory 301 and/or input commands received from a user via the I/O device 302, or commands received from the computer system 203.

It will also be assumed that the user interacts with application software executed by the processing system 210 via a GUI, or the like presented on the computer system 203. Actions performed by the computer system 203 are performed by the processor 401 in accordance with instructions stored as applications software in the memory 402 and/or input commands received from a user via the I/O device 403. The base station 201 is typically a server which communicates with the computer system 203 via a LAN, or the like, depending on the particular network infrastructure available.

However, it will be appreciated that the above-described configuration assumed for the purpose of the following examples is not essential, and numerous other configurations may be used. It will also be appreciated that the partitioning of functionality between the computer system 203, and the base station 201 may vary, depending on the particular implementation.

A second example of a process for determining biomarkers will now be described.

In this example, at step 500 reference data is acquired for a plurality of individuals with the reference data including at least data regarding a plurality of reference biomarkers, measured for each individual.

The reference data may be acquired in any appropriate manner but typically this involves obtaining gene expression product data from a plurality of individuals, selected to include individuals diagnosed with one or more conditions of interest, as well as healthy individuals. The terms “expression” or “gene expression” refer to production of RNA message or translation of RNA message into proteins or polypeptides, or both. Detection of either types of gene expression in use of any of the methods described herein is encompassed by the present invention. The conditions are typically medical, veterinary or other health status conditions and may include any illness, disease, stages of disease, disease subtypes, severities of disease, diseases of varying prognoses or the like.

In order to achieve this, gene expression product data are collected, for example by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantification process, such as a nucleic acid amplification process, including PCR (Polymerase Chain Reaction) or the like, in order to assess the activity, and in particular, level or abundance of a number of reference biomarkers. Quantified values indicative of the relative activity are then stored as part of the reference data.

Example reference biomarkers will be described in more detail below but it will be appreciated that these could include expression products such as nucleic acid or proteinaceous molecules, as well as other molecules relevant in making a clinical assessment. The number of biomarkers measured for use as reference biomarkers will vary depending upon the preferred implementation, but typically include a large number such as, 1000, 5000, 10000 or above, although this is not intended to be limiting.

The individuals also typically undergo a clinical assessment allowing any conditions to be clinically identified, and with an indication of any assessment or condition forming part of the reference data. Whilst any conditions can be assessed, in one example the process is utilized specifically to identify conditions such as SIRS, including inSIRS and ipSIRS or sepsis. It will be appreciated from the following, however, that this can be applied to a range of different conditions, and reference to SIRS or sepsis is not intended to be limiting.

Additionally, the reference data may include details of one or more phenotypic traits of the individuals and/or their relatives. Phenotypic traits can include information such as the gender, ethnicity, age, or the like. Additionally, in the case of the technology being applied to individuals other than humans, this can also include information such as designation of a species, breed or the like.

Accordingly, in one example the reference data can include for each of the reference individuals an indication of the activity of a plurality of reference biomarkers, a presence, absence degree, stage, or progression of a condition, phenotypic information such as phenotypic traits, genetic information and a physiological score such as a SOFA score.

The reference data is typically collected from individuals presenting at a medical centre with clinical signs relating to relevant any conditions of interest, and may involve follow-on consultations in order to confirm clinical assessments, as well as to identify changes in biomarkers, and/or clinical signs, and/or severity of clinical signs, over a period of time. In this latter case, the reference data can include time series data indicative of the progression of a condition, and/or the activity of the reference biomarkers, so that the reference data for an individual can be used to determine if the condition of the individual is improving, worsening or static. It will also be appreciated that the reference biomarkers are preferably substantially similar for the individuals within the sample population, so that comparisons of measured activities between individuals can be made.

It will be appreciated that once collected, the reference data can be stored in the database 211 allowing this to be subsequently retrieved by the processing system 210 for subsequent analysis. The processing system 210 also typically stores an indication of an identity of each of the reference biomarkers as a reference biomarker collection.

At step 505, the processing system 210 optionally removes a validation subgroup of individuals from the reference data prior to determining the potential biomarkers. This is performed to allow the processing system 210 to determine the potential biomarkers using the reference data without the validation subgroup so that the validation subgroup can be subsequently used to validate the potential biomarkers or signatures including a number of the potential biomarkers. Thus, data from the validation subgroup is used to validate the efficacy of the potential or signature biomarkers in identifying the presence, absence, degree, stage, severity, prognosis or progression of any one or more of the conditions to ensure the potential or signature biomarkers are effective, as will be described in more detail below.

In one example, this is achieved by having the processing system 210 flag individuals within the validation subgroup or alternatively store these in either an alternative location within the database 211 or an alternative database to the reference data. The validation subgroup of individuals is typically selected randomly and may optionally be selected to include individuals having different phenotypic traits. When a validation subgroup of individuals is removed, the remaining individuals will simply be referred to as reference data for ease throughout the remaining description.

At step 510, the individuals remaining within the reference data (ie excluding the validation subgroup) are classified into groups. The groups may be defined in any appropriate manner and may be defined based on any one or more of an indication of a presence, absence, degree, stage, severity, prognosis or progression of a condition, phenotypic traits, other tests or assays, genetic information or measured activity of the reference biomarkers associated with the individuals.

For example, a first selection of groups may be to identify one or more groups of individuals suffering from SIRS, one or more groups of individuals suffering ipSIRS, one or more groups of individuals suffering inSIRS, and one or more groups of healthy individuals. Further groups may also be defined for individuals suffering from other conditions. Additionally, further subdivision may be performed based on phenotypic traits, so groups could be defined based on gender, ethnicity or the like so that a plurality of groups of individuals suffering from a condition are defined, with each group relating to a different phenotypic trait.

It will also be appreciated, however, that identification of different groups can be performed in other manners, for example on the basis of particular activities of biomarkers within the biological samples of the reference individuals, and accordingly, reference to conditions is not intended to be limiting and other information may be used as required.

The manner in which classification into groups is performed may vary depending on the preferred implementation. In one example, this can be performed automatically by the processing system 210, for example, using unsupervised methods such as Principal Components Analysis (PCA), or supervised methods such as k-means or Self Organising Map (SOM). Alternatively, this may be performed manually by an operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI), and define respective groups using appropriate input commands.

Once the groups have been defined, analysis techniques are utilized in order to identify reference biomarkers that can be utilized to potentially distinguish the groups. The analysis technique typically examines the activity of the reference biomarkers for individuals within and across the groups, to identify reference biomarkers whose activities differ between and hence can distinguish groups. A range of different analysis techniques can be utilized including, for example, regression or correlation analysis techniques. Examples of the techniques used can include established methods for parametized model building such as Partial Least Squares, Random Forest or Support Vector Machines, usually coupled to a feature reduction technique for the selection of the specific subset of the biomarkers to be used in a signature.

Such techniques are known and described in a number of publications. For example, the use of Partial Least Squares is described in “Partial least squares: a versatile tool for the analysis of high-dimensional genomic data” by Boulesteix, Anne-Laure and Strimmer, Korbinian, from Briefings in Bioinformatics 2007 vol 8. no. 1, pg 32-44. Support Vector machines are described in “LIBSVM: a library for support vector machines” by Chang, C. C. and Lin, C. J. from ACM Transactions on Intelligent Systems and Technology (TIST), 2011 vol 2, no. 3, pg 27. Standard Random Forest in R language is described in “Classification and Regression by random Forest” by Liaw, A. and Wiener, M., in R news 2002, vol 2, no. 3, pg 18-22.

The analysis techniques are implemented by the processing system 210, using applications software, which allows the processing system 210 to perform multiple ones of the analysis techniques in sequence. This is advantageous as the different analysis techniques typically have different biases and can therefore be used to identify different potential biomarkers that can distinguish the groups, thereby reducing the risk of clinically relevant biomarkers being overlooked.

At step 515 a next analysis technique is selected by the processing system 210, with this being implemented at step 520 to identify the best N reference biomarkers for distinguishing the groups, where the variable N is a predetermined or algorithmically derived number of biomarkers whose value may vary depending on the analysis technique used and the preferred implementation, but is typically a relatively small number compared to the overall number of biomarkers, such as less than 10, more than 1, between 2 and 8 and 5. This process typically involves a predictive model to assess the ability of activities of particular ones of the reference biomarkers to distinguish between different groups. For example this can examine the manner in which the activity of reference biomarkers differ between groups, and/or are relatively similar within a group. This can be performed iteratively for different combinations of reference biomarkers until a best N of the reference biomarkers are identified.

At step 525, the processing system 210 determines the predictive performance of the identified best N reference biomarkers, when used in the model, for in distinguishing the relevant groups. The predictive performance is typically a parameter determined as part of the combination of analysis technique and chosen embodying model, as will be appreciated by persons skilled in the art. For example, receiver operating characteristic (ROC) analysis may be used to determine optimal assay parameters to achieve a specific level of accuracy, specificity, positive predictive value, negative predictive value, and/or false discovery rate.

Optionally, a cross-validation approach may be used whereby steps 520 and 525 are repeated M times to produce a distribution of M predictive performance measures, and N×M selected reference biomarkers. It will be appreciated that there may be none, some, or complete overlap in the sets of selected reference biomarkers for the M iterations. The union (unique set) of selected reference biomarkers from all M iterations is the set U.

At step 530, the predictive performance is compared to a predetermined threshold, which is typically selected dependent upon the preferred implementation, but may be a relatively low value such as 80%. In the case of cross-validation, in which steps 520 and 525 are repeated M times, the predictive performance at step 530 is some property of the M predictive performance measurements such as the mean, median or maximum.

By example, ruling in ipSIRS might have a lower threshold than ruling out ipSIRS since the clinical risk of treating someone with inSIRS with antibiotics might be considered to be less than not treating someone with ipSIRS with antibiotics. Thus, it can be appreciated that the threshold set is influenced by a variety of factors including clinical utility, patient welfare, disease prevalence, and econometrics of test use to name a few examples.

At step 535, if it is determined that the predictive performance is above the threshold, the identified N reference biomarkers are added to a list or collection of potential biomarkers, an indication of which is typically stored in the database 211. In the case of a cross-validation approach, where the set of unique selected biomarkers (U) may be larger than the number to be selected as potential biomarkers (N), the N most frequently selected biomarkers during the M iterations are identified as the N reference biomarkers and are then removed from the reference biomarker collection before further analysis is performed. The process then returns to step 520 allowing the same analysis technique to be performed and the next N reference biomarkers identified.

It will therefore be appreciated that this is an iterative technique that allows reference biomarkers capable of distinguishing the groups to be progressively identified with the ability of an additional N reference biomarkers to act as potential biomarkers being assessed, within each iteration. This process performs a relatively coarse filtering of reference biomarkers allowing groups of reference biomarkers with predictive performance above the threshold to be progressively removed from the reference biomarker collection and added to the potential biomarker collection.

During this process, if it is determined that the predictive performance of the N identified reference biomarkers is below the threshold, then the process moves to step 540 when it is determined by the processing system 210 if all analysis techniques have been used. If not, the process returns to step 515 allowing a next analysis technique to be selected.

Thus, it will be appreciated that the iterative process is repeated for a number of different analysis techniques allowing biases between the techniques to identify different potential biomarkers. Accordingly, this process progressively identifies reference biomarkers useful as potential biomarkers utilizing a coarse identification process that can be performed relatively rapidly, and optionally in parallel, over a large number of reference biomarkers.

At this stage, the potential biomarkers may be utilized in an attempt to classify the validation subgroup of individuals. In particular, the different activities of the identified potential biomarkers for individuals within each group are utilized to attempt to classify individuals in the validation subgroup into the groups defined at step 510. In the event that classification of the validation subgroup is successful, potential biomarkers may be retained, whereas if a validation is unsuccessful potential biomarkers may optionally be removed from the potential biomarker collection.

In one example, the above-described process is performed over several thousand different reference biomarkers allowing a collection of several hundred potential biomarkers to be identified. However, the potential biomarkers may not be ideal for answering specific clinical assessment questions, such as ruling in a condition, ruling out a condition, or determining a stage of progression or likely outcome of a condition or treatment.

Accordingly, once the potential biomarkers have been identified, more refined processes are used to allow the processing system 210 to identify a number of potential biomarkers for use as signature biomarkers, in turn allowing signatures to be developed for performing specific clinical assessments.

In this regard, it will be appreciated that typically clinicians will want to perform a specific clinical assessment based on a preliminary diagnosis made using clinical signs, present in a subject presented to them. Accordingly, a clinician could potentially only need to answer the question of whether the subject has ipSIRS, or does not have ipSIRS. As the cost, speed and ability to perform a diagnostic test will typically be heavily dependent on the number of biomarkers assessed as part of the test, it is preferable to be able to identify a minimal number of biomarkers that are able to answer the specific clinical assessment of interest. To address this, the process can use more refined analysis of the potential biomarkers to identify those that are most useful in performing a particular clinical assessment, and hence can be used as signature biomarkers.

Accordingly, at step 545 a next clinical assessment is determined. This can be achieved in any manner, but usually involves having the user define the clinical assessment using appropriate input commands. As part of this, at step 550, the processing system 210 is used to identify second groups that are relevant to the clinical assessment, for example, by having the user identify criteria, such as the relevant conditions associated with each group, or the stage of progression for the individuals within the groups. This could include, for example, defining groups of individuals having ipSIRS and those not having ipSIRS, or those having mild, major, worsening or improving ipSIRS. Whilst it will be appreciated that the second groups may be the same as the first groups previously defined at step 510, more typically the second groups are more appropriately targeted based on the particular clinical assessment.

At step 555, the processing system 210 uses a second analysis technique to identify a number of the potential biomarkers that best distinguish the second groups of individuals. In particular, this will attempt to identify potential biomarkers whose level of activity for the individuals within the groups, can be used to distinguish the groups. The nature of the analysis technique will vary depending upon the preferred implementation and can include analysis techniques similar to those outlined above. Alternatively, different analysis techniques can be used such as ordinal classification, which differs from regular classification in that the known order of classes is used without assumptions as to their relative similarity to impose extra constraints in the model leading to more accurate clarification of borderline cases. Such ordinal classification is described in “Support vector ordinal regression” by Chu, W. and Keerthi, S. S., in Neural Computation 2007, vol 19, no. 3, pg 792-815.

An ordinal SVM for classification consists of the same fundamental elements of any SVM technique that would be familiar to anyone skilled in the art. Namely, the objective is to describe a number of maximally separating hyper-planes in the transformed hyperspace defined by the kernel function. An ordinal classifier differs from a regular SVM classifier in that it imposes an ordinal structure through the use of the cost function. This is implemented by adding to cost functions a component which penalizes incorrect ranks during execution, as described “Support vector ordinal regression” by Chu et al. (2007, supra).

Typically, the analysis techniques are implemented to identify a limited overall number of potential biomarkers that can be used as signature biomarkers, and may therefore use more stringent criteria than the analysis techniques used in steps 515 to 530 above. Alternatively, the analysis techniques may not be limited in the number of potential biomarkers identified, and can instead identify more or less potential biomarkers than the predetermined number N, above. Additionally, for this reason, only a single analysis technique is typically required at this stage, although this is not essential and multiple second analysis techniques could be used.

At step 560, the processing system 210 determines if the predictive performance of the identified potential biomarkers exceeds a second predetermined threshold.

Optionally, a cross-validation approach may be used whereby steps 550 and 560 are repeated M times to produce a distribution of M predictive performance measures, and N×M selected reference biomarkers. It will be appreciated that there may be none, some, or complete overlap in the sets of selected reference biomarkers for the M iterations. The union (unique set) of selected reference biomarkers from all M iterations is the set U.

Optionally, a consensus approach may be used, whereby steps 555 and 560 are repeated multiple times, and the predictive performance measure is some measure of the consensus of the iterations, such as the average value.

At step 565, if it is determined that the predictive performance is not above the second predetermined threshold, the processing system 210 modifies parameters associated with the analysis technique at step 570 and the process returns to step 555 allowing the same or alternative potential biomarkers to be assessed. This process is repeated until a successful determination occurs when a limited number of potential biomarkers are identified which provide a predictive performance above the threshold, in which case the process moves on to step 575.

It will be appreciated that as this is attempting to identify a limited number of biomarkers that provide better predictive performance, the second predetermined threshold is typically set to be higher that the first predetermined threshold used at step 530, and as a result of this, the second analysis technique may be computationally more expensive. Despite this, as the process is only being performed on the basis of the potential biomarkers and not the entire set of reference biomarkers, this can typically be performed relatively easily.

At step 575, the processing system 210 determines if the identified potential biomarkers are to be excluded. This may occur for any one of a number of reasons. For example, a limited number of say five biomarkers may be identified which are capable of providing the required clinical assessment outcome. However, it may not be possible to use some of these biomarkers for legal or technical reasons, in which case the biomarkers may be excluded. In this case, the excluded biomarkers are removed from the potential biomarker database at step 580 and the process returns to step 555 allowing the analysis to be performed.

It will be appreciated that whilst such excluded biomarkers may be removed from the reference data at an earlier point in the process, the ability to identify excluded biomarkers may be difficult. For example, performing a freedom-to-operate assessment of potential biomarkers can be an expensive process. It is therefore unfeasible to do this to the entire collection of biomarkers within the reference database or even to the entire collection of potential biomarkers. Accordingly, this assessment is only typically made once a potential biomarker has been identified at step 555 to 565 as providing a predictive performance above the threshold.

In the event that none of the potential biomarkers are excluded, the identified potential biomarkers are used as signature biomarkers, and an indication of the signature biomarkers is typically stored in a signature biomarker collection in the database 211. The measured activities from the reference individuals for the signature biomarkers can then be used to generate signatures for use in performing the clinical assessment at step 585. The signatures will typically define activities or ranges of activities of the signature biomarkers that are indicative of the presence, absence, degree, stage, or progression of a condition. This allows the signatures to be used in performing diagnostic and/or prognostic assessment of subjects.

For example, an indication of the activity of the signature biomarkers can be obtained from a sample taken from a test subject, and used to derive a signature indicative of the health status of the test subject. This can then be compared to the signatures derived from the reference data to assess the likely heath status of the subject.

Following this, at step 590 the process moves on to determine whether all clinical assessments have been addressed and if not, returns to step 545 allowing a next clinical assessment to be selected. Otherwise, the process ends at step 595.

Accordingly, it will be appreciated that the above-described methodology utilizes a staged approach in order to generate potential biomarkers and optionally, further signature biomarkers, for use in performing clinical assessments.

The process utilizes an initial coarse filtering based on a plurality of analysis techniques in order to identify a limited number of potential biomarkers. The limited number of potential biomarkers, which is typically in the region of less than 500, are selected from a larger database of biomarkers as being those most capable of distinguishing between different conditions, and/or different stages or progressions of a condition.

Following this, in a further stage, specific clinical assessments are identified with additional analysis techniques being used to select particular biomarkers from the database of potential biomarkers with the particular biomarkers being capable of being use in answering the specific clinical assessments.

A specific example of the above-described process will now be described with reference to distinguishing between inSIRS and ipSIRS.

A number of patients clinically identified as having infection negative SIRS and infection positive SIRS had peripheral blood samples taken (N=141). These samples were run on microarray. The microarray data was then normalised and quality control (QC) filtered as per the recommendation of the manufacturer to produce a list of samples with a corresponding clinical diagnosis of SIRS with or without an infection (N=141), and a list of reference biomarkers that passed QC (N=15,989).

The process of building and testing a model will now be described. In this example, 10% of the samples are randomly selected to act as the testing/validation set and are put aside. The remaining 90% of the samples are the training set, used to identify the potential biomarkers.

A feature selection algorithm coupled to a machine learning model is then applied to the training set. In this example a Recursive Feature Selection Support Vector Machine, described for example in “Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data”, by Xuegong Zhang, Xin Lu, Qian Shi, Xiu-qin Xu, Hon-chiu E Leung, Lyndsay N Harris, James D Iglehart, Alexander Miron, Jun S Liu and Wing H Wong from BMC Bioinformatics 2006, 7:197, was used to build a model with exactly 10 genes as the input.

Assuming no technical or biological noise and ignoring sample size considerations, these genes best describe the inherent variability between inSIRS and ipSIRS samples when using an SVM model, and therefore provide the best available separation signature.

For each sample in the testing set, the model is used to predict either inSIRS or ipSIRS. If the prediction matches the clinical record for this sample, it is declared a correct prediction. The performance of the model in this case is measured by accuracy, which can be expressed as the percentage of correct predictions for the testing set.

Optionally, the building and testing step may be repeated with a different random testing and training set. This could be performed any number of times depending on the preferred implementation, and in one example is performed 100 times. If the accuracy of the model was not significantly better than the last 2 iterations (1 way ANOVA p-value>0.95), then the selection of biomarkers was terminated.

If the accuracy remained significantly better than either of the last 2 iterations (as described above), then the 10 genes that were selected in the model (or most frequently appear if repeated runs were used) are then added to the collection of potentially useful biomarkers, and were removed from subsequent iterations.

The biomarker identification process described above and elsewhere herein has been used to identify 319 biomarker genes (hereafter referred to as “inflammatory response syndrome (IRS) biomarker genes”), which are surrogate markers that are useful for assisting in distinguishing: (1) between SIRS affected subjects (i.e., subject having inSIRS or ipSIRS) and healthy subjects or subjects not affected by SIRS; (2) between subjects with inSIRS and subjects with ipSIRS; and/or (3) between subjects with different stages of ipSIRS (e.g., sepsis, severe sepsis and septic shock). Based on this identification, the present inventors have developed various methods and kits, which take advantage of these biomarkers to determine the likelihood of the presence or absence of a condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), SIRS generally (i.e., not distinguishing between inSIRS or ipSIRS), inSIRS or ipSIRS, or to assess the likelihood of the presence, absence or risk of development of a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, illustrative examples of which include mild sepsis, severe sepsis and septic shock). In advantageous embodiments, the methods and kits involve monitoring the expression of IRS biomarker genes in blood cells (e.g., immune cells such as leukocytes), which may be reflected in changing patterns of RNA levels or protein production that correlate with the presence of active disease or response to disease.

As used herein, the term SIRS (“systemic inflammatory response syndrome”) refers to a clinical response arising from a non-specific insult with two or more of the following measureable clinical characteristics; a body temperature greater than 38° C. or less than 36° C., a heart rate greater than 90 beats per minute, a respiratory rate greater than 20 per minute, a white blood cell count (total leukocytes) greater than 12,000 per mm³ or less than 4,000 per mm³, or a band neutrophil percentage greater than 10%. From an immunological perspective, it may be seen as representing a systemic response to insult (e.g., major surgery) or systemic inflammation. As used herein, “inSIRS” includes the clinical response noted above but in the absence of a systemic infectious process. By contrast, “ipSIRS” includes the clinical response noted above but in the presence of a presumed or confirmed systemic infectious process. Confirmation of infectious process can be determined using microbiological culture or isolation of the infectious agent. From an immunological perspective, ipSIRS may be seen as a systemic response to microorganisms be it local, peripheral or a systemic infection.

The terms “surrogate marker” and “biomarker” are used interchangeably herein to refer to a parameter whose measurement (e.g., level, presence or absence) provides information as to the state of a subject. In various exemplary embodiments, a plurality of biomarkers is used to assess a condition (e.g., healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS). Measurements of the biomarkers may be used alone or combined with other data obtained regarding a subject in order to determine the state of the subject biomarker. In some embodiments, the biomarkers are “differentially present” in a sample taken from a subject of one phenotypic status (e.g., having a specified condition) as compared with another phenotypic status (e.g., not having the condition). A biomarker may be determined to be “differentially present” in a variety of ways, for example, between different phenotypic statuses if the presence or absence or mean or median level or concentration of the biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio.

In some embodiments, the methods and kits involve: (1) correlating a reference IRS biomarker profile with the presence or absence of a condition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker; (2) obtaining an IRS biomarker profile of a sample (i.e., “a sample IRS biomarker profile”) from a subject, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile.

As used herein, the term “profile” includes any set of data that represents the distinctive features or characteristics associated with a condition of interest, such as with a particular prediction, diagnosis and/or prognosis of a specified condition as taught herein. The term generally encompasses quantification of one or more biomarkers inter alia nucleic acid profiles, such as for example gene expression profiles (sets of gene expression data that represents the mRNA levels of one or more genes associated with a condition of interest), as well as protein, polypeptide or peptide profiles, such as for example protein expression profiles (sets of protein expression data that represents the levels of one or more proteins associated with a condition of interest), and any combinations thereof.

Biomarker profiles may be created in a number of ways and may be the combination of measurable biomarkers or aspects of biomarkers using methods such as ratios, or other more complex association methods or algorithms (e.g., rule-based methods), as discussed for example in more detail below. A biomarker profile comprises at least two measurements, where the measurements can correspond to the same or different biomarkers. Thus, for example, distinct reference profiles may represent the prediction of a risk (e.g., an abnormally elevated risk) of having a specified condition as compared the prediction of no or normal risk of having that condition. In another example, distinct reference profiles may represent predictions of differing degrees of risk of having a specified condition.

The terms “subject,” “individual” and “patient” are used interchangeably herein to refer to any subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the subphylum Chordata including primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines (e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars etc), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subject is a primate (e.g., a human, ape, monkey, chimpanzee).

IRS biomarkers are suitably expression products of IRS biomarker genes, including polynucleotide and polypeptide expression products. The term “gene” as used herein refers to any and all discrete coding regions of the cell's genome, as well as associated non-coding and regulatory regions. The term “gene” is also intended to mean the open reading frame encoding specific polypeptides, introns, and adjacent 5′ and 3′ non-coding nucleotide sequences involved in the regulation of expression. In this regard, the gene may further comprise control signals such as promoters, enhancers, termination and/or polyadenylation signals that are naturally associated with a given gene, or heterologous control signals. The DNA sequences may be cDNA or genomic DNA or a fragment thereof. The gene may be introduced into an appropriate vector for extrachromosomal maintenance or for integration into the host.

As used herein, polynucleotide expression products of IRS biomarker genes are referred to herein as “IRS biomarker polynucleotides.” Polypeptide expression products of the IRS biomarker genes are referred to herein as “IRS biomarker polypeptides.”

Suitably, individual IRS biomarker genes are selected from the group consisting of: TLR5; CD177; VNN1; UBE2J1; IMP3; RNASE2//LOC643332; CLEC4D; C3AR1; GPR56; ARG1; FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2; GNLY; GALNT3; OMG; SLC37A3; BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3; CLEC4E; IL18R1; ACER3; ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53; PLB1; DSE; PTGDR; CAMK4; DNAJC13; TNFAIP6; FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5; MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2; B3GNT5//MCF2L2; GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C; C9orf72; HIST1H3I; SLC15A2; TLR10; ADM; CD274; CRIP1; LRRN3; HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP; MRPL41; SLC1A3; OLFM4; CASS4; TCN1; WSB2; CLU; ODZ1; KPNA5; PLAC8; CD63; HPSE; C1orf161; DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757; GOT2; PDGFC; B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IPO11; TMEM144//LOC285505; CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH; PPP2R5A//SNORA16B; TMTC1; EAF2//HCG11//LOC647979; RCBTB2//LOC100131993; SEC24A//SAR1B; SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7; ANAPC11; EXOSC4; gene for IL-1beta-regulated neutrophil survival protein as set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2; RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163; FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2; EIF2AK2; LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth in GenBank Accession No. AK098012; EFCAB2; HIST1H2AA; HINT1; HIST1H3J; CDA; SAP30; AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2; GAB2; HS2ST1//UBA2; HIST1H3A; C22orf37; HLA-DPA1; VOPP1//LOC100128019; SLC39A8; MKI67; SLC11A1; AREG; ABCA1; DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2; CEACAM8; CLEC4A; PMS2CL//PMS2; REIN; PDE3B; SULF2; NEK6//LOC100129034; CENPK; TRAF3; GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP; CD24; GALNT2; HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A; ACTA2; ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL; EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4; MINPP1; TRIM21; ZNF28; NPCDR1; gene for protein FLJ21394 as set forth in GenBank Accession No. BC013935; gene for transcript set forth in GenBank Accession No. AK000992; ICAM1; TAF13; P4HA1//RPL17; C15orf54; KLHL5; HAL; DLEU2//DLEU2L; ANKRD28; LY6G5B//CSNK2B; KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746; HSPB1//HSPBL2; CCR4; TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM; PDK4; ABCG1; IL1B; THBS1; ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B; ZRANB1; TIMM10; FSD1L//GARNL1; HIST1H2AJ//HIST1H2AI; PTGS1; gene for transcript set forth in GenBank Accession No. BC008667; UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A; CCRL2; E2F6; MPZL3; SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2; 5100B; ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1; FABP2; KDM6B//TMEM88; IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682; MYL9; HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2; MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript set forth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2; FAM118B; B4GALT3; RASA4//RASA4P//RASA4B//POLR2J4//LOC100132214; CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17; FBXL13; PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2; ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD; MPZL2; LOC100128751; IGJ; CDC26; PPP1R2//PPP1R2P3; IL5RA; ARL17P1//ARL17; ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D; CALM2//C2orf61; SPATA6; IGLV6-57; C1orf128; KRTAP15-1; IFI44; IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536; gene for transcript set forth in GenBank Accession No. BC034024; SDHC; NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1.

As used herein, the term “likelihood” is used as a measure of whether subjects with a particular IRS biomarker profile actually have a condition (or not) based on a given mathematical model. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased risk may be expressed as simply determining the subject's level of a given IRS biomarker and placing the test subject in an “increased risk” category, based upon previous population studies. Alternatively, a numerical expression of the test subject's increased risk may be determined based upon IRS biomarker level analysis.

As used herein, the term “probability” refers strictly to the probability of class membership for a sample as determined by a given mathematical model and is construed to be equivalent likelihood in this context.

In some embodiments, likelihood is assessed by comparing the level or abundance of individual IRS biomarkers to one or more preselected or threshold levels. Thresholds may be selected that provide an acceptable ability to predict diagnosis, prognostic risk, treatment success, etc. In illustrative examples, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable versus its relative frequency in two populations in which a first population has a first condition or risk and a second population has a second condition or risk (called arbitrarily, for example, “healthy condition” and “SIRS,” “healthy condition” and “inSIRS,” “healthy condition” and “ipSIRS,” “inSIRS” and “ipSIRS,” “mild sepsis” and “severe sepsis,” “severe sepsis” and “septic shock,” “mild sepsis” and “septic shock,” or “low risk” and “high risk”).

For any particular IRS biomarker, a distribution of IRS biomarker levels for subjects with and without a disease will likely overlap. Under such conditions, a test does not absolutely distinguish a first condition and a second condition with 100% accuracy, and the area of overlap indicates where the test cannot distinguish the first condition and the second condition. A threshold is selected, above which (or below which, depending on how an IRS biomarker changes with a specified condition or prognosis) the test is considered to be “positive” and below which the test is considered to be “negative.” The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et al., Radiology 143: 29-36 (1982).

Alternatively, or in addition, thresholds may be established by obtaining an earlier biomarker result from the same patient, to which later results may be compared. In these embodiments, the individual in effect acts as their own “control group.” In biomarkers that increase with condition severity or prognostic risk, an increase over time in the same patient can indicate a worsening of the condition or a failure of a treatment regimen, while a decrease over time can indicate remission of the condition or success of a treatment regimen.

In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and/or AUC or receiver operating characteristic (ROC) values are used as a measure of a method's ability to predict risk or to diagnose a disease or condition. As used herein, the term “likelihood ratio” is the probability that a given test result would be observed in a subject with a condition of interest divided by the probability that that same result would be observed in a patient without the condition of interest. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified condition divided by the probability of a positive results in subjects without the specified condition. A negative likelihood ratio is the probability of a negative result in subjects without the specified condition divided by the probability of a negative result in subjects with specified condition. The term “odds ratio,” as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., a healthy condition group) to the odds of it occurring in another group (e.g., a SIRS group, an inSIRS group, an ipSIRS group, or a group with particular stage of ipSIRS), or to a data-based estimate of that ratio. The term “area under the curve” or “AUC” refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a healthy condition IRS biomarker profile and a SIRS, inSIRS, ipSIRS, or ipSIRS stage IRS biomarker profile). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the IRS biomarkers described herein and/or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., cases having a condition and controls without the condition). Typically, the feature data across the entire population (e.g., the cases and controls) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, “AUC ROC values” are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be thought of as equivalent to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.

In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker or a panel if IRS biomarkers is selected to discriminate between subjects with a first condition and subjects with a second condition with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.

In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the “condition” and “control” groups; a value greater than 1 indicates that a positive result is more likely in the condition group; and a value less than 1 indicates that a positive result is more likely in the control group. In this context, “condition” is meant to refer to a group having one characteristic (e.g., the presence of a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS) and “control” group lacking the same characteristic. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the “condition” and “control” groups; a value greater than 1 indicates that a negative result is more likely in the “condition” group; and a value less than 1 indicates that a negative result is more likely in the “control” group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the “condition” and “control” groups; a value greater than 1 indicates that a positive result is more likely in the “condition” group; and a value less than 1 indicates that a positive result is more likely in the “control” group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a IRS biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest, while 1.0 indicates the relatively best diagnostic accuracy. In certain embodiments, IRS biomarkers and/or IRS biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.

In certain embodiments, IRS biomarkers and/or IRS biomarker panels are selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.

In certain embodiments, IRS biomarkers and/or IRS biomarker panels are selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.

In some cases, multiple thresholds may be determined in so-called “tertile,” “quartile,” or “quintile” analyses. In these methods, the “diseased” and “control groups” (or “high risk” and “low risk”) groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) “bins” having equal numbers of individuals. The boundary between two of these “bins” may be considered “thresholds.” A risk (of a particular diagnosis or prognosis for example) can be assigned based on which “bin” a test subject falls into.

In other embodiments, particular thresholds for the IRS biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to a particular diagnosis or prognosis. For example, a temporal change in the biomarker(s) can be used to rule in or out one or more particular diagnoses and/or prognoses. Alternatively, IRS biomarker(s) are correlated to a condition, disease, prognosis, etc., by the presence or absence of one or more IRS biomarkers in a particular assay format. In the case of IRS biomarker panels, the present invention may utilize an evaluation of the entire profile of IRS biomarkers to provide a single result value (e.g., a “panel response” value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of IRS biomarkers may be sufficient to indicate a particular condition or future outcome in one patient, while an increase, decrease, or other change in a different subset of IRS biomarkers may be sufficient to indicate the same or a different condition or outcome in another patient.

In certain embodiments, a panel of IRS biomarkers is selected to assist in distinguishing a pair of groups (i.e., assist in assessing whether a subject has an increased likelihood of being in one group or the other group of the pair) selected from “healthy condition” and “SIRS,” “healthy condition” and “inSIRS,” “healthy condition” and “ipSIRS,” “inSIRS” and “ipSIRS,” “mild sepsis” and “severe sepsis,” “severe sepsis” and “septic shock,” “mild sepsis” and “septic shock,” or “low risk” and “high risk” with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.

The phrases “assessing the likelihood” and “determining the likelihood,” as used herein, refer to methods by which the skilled artisan can predict the presence or absence of a condition (e.g., a condition selected from healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS) in a patient. The skilled artisan will understand that this phrase includes within its scope an increased probability that a condition is present or absence in a patient; that is, that a condition is more likely to be present or absent in a subject. For example, the probability that an individual identified as having a specified condition actually has the condition may be expressed as a “positive predictive value” or “PPV.” Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods of the present invention as well as the prevalence of the condition in the population analysed. The statistical algorithms can be selected such that the positive predictive value in a population having a condition prevalence is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In other examples, the probability that an individual identified as not having a specified condition actually does not have that condition may be expressed as a “negative predictive value” or “NPV.” Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of the disease in the population analysed. The statistical methods and models can be selected such that the negative predictive value in a population having a condition prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some embodiments, a subject is determined as having a significant likelihood of having or not having a specified condition. By “significant likelihood” is meant that the subject has a reasonable probability (0.6, 0.7, 0.8, 0.9 or more) of having, or not having, a specified condition.

The IRS biomarker analysis of the present invention permits the generation of high-density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, et.) IRS biomarker in an IRS biomarker profile with respect to a condition selected from healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS. For example, methods such as multivariate analysis of variance, multivariate regression, and/or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of IRS biomarkers). Clustering, including both hierarchical and non-hierarchical methods, as well as non-metric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.

In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing “hidden” variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore, simple hypothesis such as equality of two vectors of means can be tested using Hotelling's T squared statistic.

In some embodiments, the data sets corresponding to IRS biomarker profiles are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between an IRS biomarker profile and a condition selected from healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference IRS biomarker profiles for comparison with IRS biomarker profiles of a subject. The data are used to infer relationships that are then used to predict the status of a subject, including the presence or absence of one of the conditions referred to above.

Practitioners skilled in the art of data analysis recognize that many different forms of inferring relationships in the training data may be used without materially changing the present invention. The data presented in the Tables and Examples herein has been used to generate illustrative minimal combinations of IRS biomarkers (models) that differentiate between two conditions selected from healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS using feature selection based on AUC maximisation in combination with support vector machine classification. Tables 1-15 provide illustrative lists of IRS biomarkers ranked according to their p value and FIGS. 1-331 illustrate the ability of each IRS biomarker to distinguish between at least two of the conditions. Illustrative models comprising at least about 2 IRS biomarkers were able to discriminate between two control groups as defined above with significantly improved positive predictive values compared to conventional methodologies.

The term “correlating” generally refers to determining a relationship between one type of data with another or with a state. In various embodiments, correlating an IRS biomarker profile with the presence or absence of a condition (e.g., a condition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS) comprises determining the presence, absence or amount of at least one IRS biomarker in a subject that suffers from that condition; or in persons known to be free of that condition. In specific embodiments, a profile of IRS biomarker levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.

Thus, in some embodiments, evaluation of IRS biomarkers includes determining the levels of individual IRS biomarkers, which correlate with the presence or absence of a condition, as defined above. In certain embodiments, the techniques used for detection of IRS biomarkers will include internal or external standards to permit quantitative or semi-quantitative determination of those biomarkers, to thereby enable a valid comparison of the level of the IRS biomarkers in a biological sample with the corresponding IRS biomarkers in a reference sample or samples. Such standards can be determined by the skilled practitioner using standard protocols. In specific examples, absolute values for the level or functional activity of individual expression products are determined.

In semi-quantitative methods, a threshold or cut-off value is suitably determined, and is optionally a predetermined value. In particular embodiments, the threshold value is predetermined in the sense that it is fixed, for example, based on previous experience with the assay and/or a population of affected and/or unaffected subjects. Alternatively, the predetermined value can also indicate that the method of arriving at the threshold is predetermined or fixed even if the particular value varies among assays or may even be determined for every assay run.

In some embodiments, the level of an IRS biomarker is normalized against a housekeeping biomarker. The term “housekeeping biomarker” refers to a biomarker or group of biomarkers (e.g., polynucleotides and/or polypeptides), which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed. In some embodiments, the housekeeping biomarker is a “housekeeping gene.” A “housekeeping gene” refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed.

Generally, the levels of individual IRS biomarkers in an IRS biomarker profile are derived from a biological sample. The term “biological sample” as used herein refers to a sample that may be extracted, untreated, treated, diluted or concentrated from an animal. The biological sample is suitably a biological fluid such as whole blood, serum, plasma, saliva, urine, sweat, ascitic fluid, peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal fluid, tissue biopsy, and the like. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof. Typically, the biological sample comprises blood cells such as mature, immature or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes, macrophages, dendritic cells natural killer cells, or fraction of such cells (e.g., a nucleic acid or protein fraction). In specific embodiments, the biological sample comprises leukocytes including peripheral blood mononuclear cells (PBMC).

The term “nucleic acid” or “polynucleotide” refers to a polymer, typically a heteropolymer, of nucleotides or the sequence of these nucleotides from the 5′ to 3′ end of a nucleic acid molecule and includes DNA or RNA molecules, illustrative examples of which include RNA, mRNA, siRNA, miRNA, hpRNA, cRNA, cDNA or DNA. The term encompasses a polymeric form of nucleotides that is linear or branched, single or double stranded, or a hybrid thereof. The term also encompasses RNA/DNA hybrids. Nucleic acid sequences provided herein are presented herein in the 5′ to 3′ direction, from left to right and are represented using the standard code for representing the nucleotide characters as set forth in the U.S. sequence rules, 37 CFR 1.821-1.825 and the World Intellectual Property Organization (WIPO) Standard ST.25.

“Protein,” “polypeptide” and “peptide” are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.

Suitably, the levels of individual IRS biomarkers in a reference IRS biomarker profile are derived from IRS biomarker samples obtained from one or more control subjects having that condition (e.g., “healthy control subjects,” “SIRS control subjects,” “inSIRS control subjects,” “ipSIRS control subjects,” “control subjects with a particular stage of ipSIRS,” illustrative examples of which include “mild sepsis control subjects,” “severe sepsis control subjects,” and “septic shock control subjects,” etc.), which are also referred to herein as control groups (e.g., “healthy control group,” “SIRS control group,” “inSIRS control group,” “ipSIRS control group,” “ipSIRS stage group,” illustrative examples of which include “mild sepsis control group,” “severe sepsis control group,” and “septic shock control group,” etc.). By “obtained” is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

As used herein the terms “level” and “amount” are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class or a ratio), a concentration, and the like. Thus, these terms encompasses absolute or relative amounts or concentrations of IRS biomarkers in a sample, including ratios of levels of IRS biomarkers, and odds ratios of levels or ratios of odds ratios. IRS biomarker levels in cohorts of subjects may be represented as mean levels and standard deviations as shown in the Tables and Figures herein.

In some embodiments, the level of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker of the subject's sample IRS biomarker profile is compared to the level of a corresponding IRS biomarker in the reference IRS biomarker profile. By “corresponding IRS biomarker” is meant an IRS biomarker that is structurally and/or functionally similar to a reference IRS biomarker. Representative corresponding IRS biomarkers include expression products of allelic variants (same locus), homologs (different locus), and orthologs (different organism) of reference IRS biomarker genes. Nucleic acid variants of reference IRS biomarker genes and encoded IRS biomarker polynucleotide expression products can contain nucleotide substitutions, deletions, inversions and/or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference IRS polypeptide.

Generally, variants of a particular IRS biomarker gene or polynucleotide will have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters. In some embodiments, the IRS biomarker gene or polynucleotide displays at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to a nucleotide sequence selected from any one of SEQ ID NO: 1-319.

Corresponding IRS biomarkers also include amino acid sequence that displays substantial sequence similarity or identity to the amino acid sequence of a reference IRS biomarker polypeptide. In general, an amino acid sequence that corresponds to a reference amino acid sequence will display at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence selected from any one of SEQ ID NO: 320-619.

In some embodiments, calculations of sequence similarity or sequence identity between sequences are performed as follows:

To determine the percent identity of two amino acid sequences, or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparison, when a position in the first sequence is occupied by the same or similar amino acid residue (i.e., conservative substitution) at the corresponding position in the second sequence, then the molecules are similar at that position.

The percent identity between the two sequences is a function of the number of identical amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. By contrast, the percent similarity between the two sequences is a function of the number of identical and similar amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.

The comparison of sequences and determination of percent identity or percent similarity between sequences can be accomplished using a mathematical algorithm. In certain embodiments, the percent identity or similarity between amino acid sequences is determined using the Needleman and Wunsch, (1970, J. Mol. Biol. 48: 444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. An non-limiting set of parameters (and the one that should be used unless otherwise specified) includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.

In some embodiments, the percent identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.

The nucleic acid and protein sequences described herein can be used as a “query sequence” to perform a search against public databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul, et al., (1990, J. Mol. Biol, 215: 403-10). BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to 53010 protein molecules of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res, 25: 3389-3402). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

Corresponding IRS biomarker polynucleotides also include nucleic acid sequences that hybridize to reference IRS biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term “hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions” describes conditions for hybridization and washing. “Hybridization” is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms “match” and “mismatch” as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1% v/v to at least about 15% v/v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42° C., and at least about 1 M to at least about 2 M salt for washing at 42° C. Low stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45□□C, followed by two washes in 0.2×SSC, 0.1% SDS at least at 50° C. (the temperature of the washes can be increased to 55° C. for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v/v to at least about 30% v/v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42° C., and at least about 0.1 M to at least about 0.2 M salt for washing at 55° C. Medium stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at 60-65° C. One embodiment of medium stringency conditions includes hybridizing in 6×SSC at about 45□□C, followed by one or more washes in 0.2×SSC, 0.1% SDS at 60° C. High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42° C., and about 0.01 M to about 0.02 M salt for washing at 55° C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), SDS for hybridization at 65° C., and (i) 0.2×SSC, 7% 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 1% SDS for washing at a temperature in excess of 65° C. One embodiment of high stringency conditions includes hybridizing in 6×SSC at about 45□□C, followed by one or more washes in 0.2×SSC, 0.1% SDS at 65° C.

In certain embodiments, a corresponding IRS biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65° C.

Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al., supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to 1.104.

Thus, in some embodiments, IRS biomarker levels in control groups as broadly defined above and elsewhere herein are used to generate a profile of IRS biomarker levels reflecting difference between levels in two control groups as described above and elsewhere herein. Thus, a particular IRS biomarker may be more abundant or less abundant in one control group as compared to another control group. The data may be represented as an overall signature score or the profile may be represented as a barcode or other graphical representation to facilitate analysis or diagnosis or determination of likelihood. The IRS biomarker levels from a test subject may be represented in the same way and the similarity with the signature score or level of “fit” to a signature barcode or other graphical representation may be determined. In other embodiments, the levels of a particular IRS biomarker are analysed and a downward or an upward trend in IRS biomarker level determined.

In some embodiments, the individual level of an IRS biomarker in a first control group (e.g., a control group selected from healthy condition control group, SIRS control group, inSIRS control group, ipSIRS control group, or ipSIRS stage control group) is at least 101%, 102%, 103%, 104%, 105%, 106%, 107% 108%, 109%, 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900% or 1000% (i.e. an increased or higher level), or no more than about 99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% (i.e. a decreased or lower level) of the level of a corresponding IRS biomarker in a second control group (e.g., a control group selected from healthy condition control group, SIRS control group, inSIRS control group, ipSIRS control group, or ipSIRS stage control group, illustrative examples of which include “mild sepsis control group, severe sepsis control group, and septic shock control group, which is different from the first control group).

An IRS biomarker profile provides a compositional analysis (e.g., concentration or mole percentage (%) of the IRS biomarker) in which two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twelve or more, fifteen or more, twenty or more, fifty or more, one-hundred or more or a greater number of IRS biomarkers are evaluated.

The IRS biomarker profile can be quantitative, semi-quantitative and/or qualitative. For example, the IRS biomarker profile can evaluate the presence or absence of an IRS biomarker, can evaluate the presence of an IRS biomarker(s) above or below a particular threshold, and/or can evaluate the relative or absolute amount of an IRS biomarker(s). In particular embodiments, a ratio among two, three, four or more IRS biomarkers is determined (see Example 6 and Tables 16-21 for examples of the use of 2-gene ratios in separating various inSIRS and ipSIRS conditions). Changes or perturbations in IRS biomarker ratios can be advantageous in indicating where there are blocks (or releases of such blocks) or other alterations in cellular pathways associated with an IRS condition, response to treatment, development of side effects, and the like.

IRS biomarkers may be quantified or detected using any suitable technique including nucleic acid- and protein-based assays.

In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al., 1989, supra; and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A⁺ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the IRS biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., (“PCR Protocols”, Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate IRS biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90/07641. Polymerase chain reaction methodologies are well known in the art.

In certain advantageous embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.

In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting or RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by “blotting” on to the filter. Subsequently, the blotted target is incubated with a probe (usually labelled) under conditions that promote denaturation and rehybridisation. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection/quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of a IRS biomarker nucleic acid detected with the progression or severity of the disease.

Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analysing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to IRS biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed IRS biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more IRS biomarker polynucleotides under conditions favouring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., et al., 1989, supra.

Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

Usually the target IRS biomarker polynucleotides are detectably labelled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labelled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu³⁴), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a coloured colloidal metallic or non metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focussed beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated colour spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different IRS biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’. A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al (U.S. Pat. No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al (U.S. Pat. No. 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labelled microbeads, the reaction may be detected using flow cytometry.

In other embodiments, IRS biomarker protein levels are assayed using protein-based assays known in the art. For example, when an IRS biomarker protein is an enzyme, the protein can be quantified based upon its catalytic activity or based upon the number of molecules of the protein contained in a sample. Antibody-based techniques may be employed including, for example, immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).

In specific embodiments, protein-capture arrays that permit simultaneous detection and/or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector. Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays, to profile protein expression in bodily fluids, such as in sera of healthy or diseased subjects, as well as in subjects pre- and post-drug treatment.

Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, BioRad and Sigma). Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210). The antigen-binding molecules of such arrays may recognise at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.

Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include colour coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.

In operation, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186), is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.

For analysing differential expression of proteins between two cells or cell populations, a protein sample of a first cell or population of cells is delivered to the array under conditions suitable for protein binding. In an analogous manner, a protein sample of a second cell or population of cells to a second array is delivered to a second array that is identical to the first array. Both arrays are then washed to remove unbound or non-specifically bound components of the sample from the arrays. In a final step, the amounts of protein remaining bound to the features of the first array are compared to the amounts of protein remaining bound to the corresponding features of the second array. To determine the differential protein expression pattern of the two cells or populations of cells, the amount of protein bound to individual features of the first array is subtracted from the amount of protein bound to the corresponding features of the second array.

All the essential materials and reagents required for detecting and quantifying IRS biomarker expression products may be assembled together in a kit, which is encompassed by the present invention. The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) an IRS biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to an IRS biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (Reverse Transcriptase, Taq, Sequenase™, DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) an IRS biomarker polypeptide (which may be used as a positive control), (ii) an antibody that binds specifically to an IRS biomarker polypeptide. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify the expression of an IRS biomarker gene.

In some embodiments, the methods and kits comprise or enable: comparing the level of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker in the subject's sample IRS profile to the level of a corresponding IRS biomarker in a reference IRS biomarker profile from at least one control subject or group selected from a healthy control subject or group (hereafter referred to as a “reference healthy IRS biomarker profile”), a SIRS control subject or group (hereafter referred to as a “reference SIRS IRS biomarker profile”), an inSIRS control subject or group (hereafter referred to as a “reference inSIRS IRS biomarker profile”), an ipSIRS control subject or group (hereafter referred to as a “reference ipSIRS IRS biomarker profile”) and a control subject or group with a particular stage of ipSIRS (hereafter referred to as a “reference ipSIRS stage IRS biomarker profile”), wherein a similarity between the level of the at least one IRS biomarker in the sample IRS biomarker profile and the level of the corresponding IRS biomarker in the reference healthy IRS biomarker profile identifies that the subject has an IRS biomarker profile that correlates with the presence of a healthy condition, or alternatively the absence of inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein a similarity between the level of the at least one IRS biomarker in the sample IRS biomarker profile and the level of the corresponding IRS biomarker in the SIRS IRS biomarker profile identifies that the subject has an IRS biomarker profile that correlates with the presence of inSIRS or ipSIRS, or alternatively the absence of a healthy condition, wherein a similarity between the level of the at least one IRS biomarker in the sample IRS biomarker profile and the level of the corresponding IRS biomarker in the inSIRS IRS biomarker profile identifies that the subject has an IRS biomarker profile that correlates with the presence of inSIRS, or alternatively the absence of a healthy condition, ipSIRS, or a particular stage of ipSIRS, wherein a similarity between the level of the at least one IRS biomarker in the sample IRS biomarker profile and the level of the corresponding IRS biomarker in the ipSIRS IRS biomarker profile identifies that the subject has an IRS biomarker profile that correlates with the presence of ipSIRS, or alternatively the absence of a healthy condition or inSIRS, and wherein a similarity between the level of the at least one IRS biomarker in the sample IRS biomarker profile and the level of the corresponding IRS biomarker in the ipSIRS stage IRS biomarker profile identifies that the subject has an IRS biomarker profile that correlates with the presence of a particular stage of ipSIRS, or alternatively the absence of a healthy condition or inSIRS.

A subset of the instantly disclosed IRS biomarkers has been identified as being useful for assisting in distinguishing between healthy subjects and unhealthy subjects that have SIRS (i.e., sick subjects with either inSIRS or ipSIRS). Thus, in some embodiments, the methods and kits involve determining the likelihood that SIRS or a healthy condition (e.g., a normal condition or a condition in which SIRS is absent) is present or absent in a subject. These methods and kits generally comprise or involve: 1) providing a correlation of a reference IRS biomarker profile with the presence or absence of SIRS or the healthy condition, wherein the reference biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected from CD177, CLEC4D, BMX, VNN1, GPR84, ARG1, IL18R1, ERLIN1, IMP3, TLR5, UBE2J1, GPR56, FCGR1A, SLC1A3, SLC37A3, FAIM3, C3AR1, RNASE2, TNFAIP6, GNLY, OMG, FAR2, OLAH, CAMK4, METTL7B, B3GNT5, CLEC4E, MMP9, KREMEN1, GALNT3, PTGDR, TDRD9, GK3P, FKBP5, STOM, SMPDL3A, PFKFB2, ANKRD34B, SGMS2, DNAJC13, LRRN3, SH2D1B, C1orf161, HIST1H4C, IFI16, ACER3, PLB1, C9orf72, HMGB2, KLRK1, C7orf53, GOT2, TCN1, DSE, CCR3, CRIP1, ITK, KLRF1, TGFBR1, GSR, HIST1H4E, HPGD, FRMD3, ABCA13, C11orf82, PPP2R5A, BPI, CASS4, AP3B2, ODZ1, TMTC1, ADM, FGFBP2, HSPC159, HLA-DRA, HIST1H3I, TMEM144, MRPL41, FOLR3, PICALM, SH3PXD2B, DDAH2, HLA-DPB1, KPNA5, PHOSPHO1, TPST1, EIF2AK2, OR9A2, OLFM4, CD163, CDA, CHI3L1, MTHFS, CLU, ANAPC11, JUP, PMAIP1, GIMAP7, KLRD1, CCR1, CD274, EFCAB2, SUCNR1, KCNMA1, LGALS2, SLC11A1, FOXD4L3, VAMP2, ITGA4, LHFP, PRR13, FFAR2, B3GAT3, EAF2, HPSE, CLC, TLR10, CCR4, HIST1H3A, CENPK, DPH3, HLA-DPA1, ATP13A3, DNAJC9, S100B, HIST1H3J, 110, RPL17, C15orf54, LRRC70, IL5RA, PLA2G7, ECHDC3, HINT1, LCN2, PPIF, SLC15A2, PMS2CL, HIST1H2AA, CEACAM8, HSP90AB1, ABCG1, PDGFC, NPCDR1, PDK4, GAB2, WSB2, FAM118A, JKAMP, TREML1, PYHIN1, IRF4, ABCA1, DAAM2, ACPL2, RCBTB2, SAP30, THBS1, PCOLCE2, GPR65, NF-E4, LTF, LASS4, B4GALT3, RETN, TIMM10, IL1B, CLEC4A, SEC24A, RUNX2, LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2, EXOSC4, CCDC125, LOC284757, ANKRD28, HIST1H2AJ, CD63, PLIN2, SON, HIST1H4L, KRTAP15-1, DLEU2, MYL9, FABP2, CD24, MACF1, GSTO1, RRP12, AIG1, RASA4, FBXL13, PDE3B, CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6, CYP4F3, 199, TAAR1, TSHZ2, PLB1, UBE2F (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker, and (3) determining a likelihood of the subject having or not having the healthy condition or SIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In illustrative examples of this type, a reference healthy condition IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference SIRS IRS biomarker profile, illustrative examples of which include: GNLY, GPR56, KLRF1, HIST1H2AJ, HIST1H4C, KLRK1, CHI3L1, SH2D1B, PTGDR, CAMK4, FAIM3, CRIP1, CLC, HLA-DPB1, FGFBP2, HIST1H3J, IMP3, ITK, HIST1H3I, LRRN3, KLRD1, PHOSPHO1, CCR3, HIST1H4E, MRPL41, HIST1H3A, HLA-DRA, GIMAP7, KPNA5, CENPK, HLA-DPA1, HINT1, HIST1H4L, GOT2, DNAJC9, PLA2G7, CASS4, CFD, ITGA4, HSP90AB1, IL5RA, PMAIP1, LGALS2, SULF2, C1orf128, RPL17, EIF1AX, PYHIN1, S100B, PMS2CL, CCR4, C15orf54, VAMP2, ANAPC11, B3GAT3, E2F6, NPCDR1, FAM118A, PPIF, 199, JUP, B4GALT3, TIMM10, RUNX2, RASA4, SON, ABCG1, TSHZ2, IRF4, PDE3B, RRP12, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided).

In other illustrative examples, a reference healthy condition IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed relative to a reference SIRS IRS biomarker profile, non-limiting examples of which include: CD177, ARG1, VNN1, CLEC4D, GPR84, IL18R1, OLFM4, FCGR1A, RNASE2, TLR5, TNFAIP6, PFKFB2, C3AR1, TCN1, BMX, FKBP5, TDRD9, OLAH, ERLIN1, LCN2, MMP9, BPI, CEACAM8, CLEC4E, HPGD, CD274, GK3P, KREMEN1, ANKRD34B, SLC37A3, CD163, TMTC1, PLB1, UBE2J1, TPST1, B3GNT5, SMPDL3A, FAR2, ACER3, ODZ1, HMGB2, LTF, SGMS2, EIF2AK2, TMEM144, GALNT3, DNAJC13, IFI16, C11orf82, ABCA13, CD24, METTL7B, FOLR3, C7orf53, SLC1A3, DAAM2, HSPC159, OMG, CCR1, TREML1, STOM, CEACAM6, FOXD4L3, C9orf72, GSR, DSE, THBS1, SH3PXD2B, PDGFC, KCNMA1, PICALM, TLR10, PDK4, ADM, CLU, C1orf161, NF-E4, HPSE, FFAR2, PPP2R5A, CDA, NA, ATP13A3, ABCA1, TGFBR1, OR9A2, EFCAB2, EAF2, AP3B2, SLC15A2, ECHDC3, MTHFS, IL1B, WSB2, SUCNR1, DDAH2, CLEC4A, MACF1, MYL9, IL1RL1, EXOSC4, FBXL13, LOC284757, PRR13, DPH3, SLC11A1, FRMD3, ACPL2, PLB1, RETN, RCBTB2, CD63, CYP4F3, SEC24A, ZRANB1, CCDC125, PCOLCE2, JKAMP, LRRFIP1, GPR65, ANKRD28, LRRC70, AIG1, UBE2F, GAB2, CCRL2, SAP30, DLEU2, HIST1H2AA, GSTO1, PLIN2, LHFP, KRTAP15-1, TAAR1, FABP2 (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference healthy condition IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference SIRS IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference SIRS IRS biomarker profile, as broadly described above.

The term “upregulated,” “overexpressed” and the like refer to an upward deviation in the level of expression of an IRS biomarker as compared to a baseline expression level of a corresponding IRS biomarker in a control sample.

The term “downregulated,” “underexpressed” and the like refer to a downward deviation in the level of expression of an IRS biomarker as compared to a baseline expression level of a corresponding IRS biomarker in a control sample.

Another subset of the instantly disclosed IRS biomarkers has been identified as being useful for assisting in distinguishing between healthy subjects, inSIRS affected subjects and subjects having ipSIRS. Accordingly, in some embodiments, the methods and kits are useful for determining the likelihood that inSIRS, ipSIRS or a healthy condition (e.g., a normal condition or a condition in which SIRS is absent) is present or absent in a subject. These methods and kits generally comprise or involve: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having inSIRS, ipSIRS or the healthy condition, wherein the reference biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected from PLAC8, 132, INSIG1, CDS2, VOPP1, SLC39A9, B3GAT3, CD300A, OCR1, PTGER2, LGALS1, HIST1H4L, AMFR, SIAE, SLC39A8, TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3, KIAA1257, OMG, HIST1H2BM, TDRD9, C22orf37, GALNT3, SYNE2, MGST3, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, HSPB1, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, KREMEN1, IRS2, APOLD1, RBP7, DNAJC13, ERGIC1, FSD1L, TLR5, TMEM62, SDHC, C9orf72, NP, KIAA0746, PMAIP1, DSE, SMPDL3A, DNAJC9, HIST1H3H, CDC26, CRIP1, FAR2, FRMD3, RGS2, METTL7B, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, IRF1, C7orf53, IGK@, RNASE2, FCGR1A, SAP30, PMS2CL, SLC11A1, AREG, PLB1, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125, GPR56, PPP1R2, 110, ACPL2, HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J, KLRD1, GPR84, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, HSP90AB1, HPGD, IFI16, CD177, TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3, HIST1H2AA, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having inSIRS, ipSIRS or a healthy condition the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In illustrative examples of this type, a reference healthy condition IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference inSIRS IRS biomarker profile, representative examples of which include: CD177, CLEC4E, FKBP5, CD163, TPST1, DAAM2, GPR84, FCGR1A, IFI16, RNASE2, TLR5, ECHDC3, OCR1, MME, LOC284757, 110, C3AR1, HAL, PRR13, ACPL2, SLC11A1, CYP4F3, SAP30, OLFM4, ZRANB1, GAB2, CCDC125, KREMEN1, UBE2J1, AREG, FAR2, CPM, PLB1, ERGIC1, RGS2, 132, HPGD, ANKRD34B, TDRD9, DNAJC13, GALNT3, IRS2, HIST1H2AA, RBP7, KDM6B, ACER3, MPZL3, KIAA1257, C7orf53, C9orf72, STOM, METTL7B, SMPDL3A, GSR, SYNE2, OMG, DSE, PICALM, ABCA13, PPP1R2, TGFBR1, AP3B2, FRMD3 (where if a gene name is not provided then a SEQ ID NO. is provided).

In other illustrative examples, a reference healthy condition IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference inSIRS IRS biomarker profile, illustrative examples of which include: SIAE, FSD1L, GLDC, HSPB1, HIST1H2BJ, CDS2, CASS4, DCTN5, SLC39A9, CDC26, LGALS1, CD151, NP, TYMS, IGLV6-57, TMEM62, CD300A, LASS4, GLT25D1, IRF1, AM FR, IGL@, NFXL1, SLC39A8, APOLD1, TNFRSF17, KIAA0101, C22orf37, VOPP1, KLRD1, TRAF3, RRP12, PTGER2, KIAA0746, MGST3, CHI3L1, TAS2R31, SDHC, IRF4, INSIG1, PPIF, B4GALT3, ANAPC11, PLAC8, HIST1H2BM, KLRF1, B3GAT3, C7orf58, PMS2CL, PTGDR, RPL17, EIF1AX, PMAIP1, HIST1H3B, IGK@, HINT1, HSP90AB1, GPR56, HIST1H3H, HIST1H3A, IMP3, DNAJC9, MRPL41, HIST1H3J, HIST1H3C, HIST1H3I, HIST1H4L, CRIP1, HIST1H4C (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference healthy condition IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference inSIRS IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference inSIRS IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference inSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference ipSIRS IRS biomarker profile, representative examples of which include: OLFM4, PLAC8, HIST1H4L, HIST1H3C, TDRD9, IGK@, HIST1H3B, HIST1H2BM, HPGD, GPR84, TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1, DNAJC9, ABCA13, ANKRD34B, RNASE2, FCGR1A, HIST1H3H, KIAA0746, ACER3, SDHC, CRIP1, IGLV6-57, PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, PTGER2, TYMS, LGALS1, C7orf58, CD151, KREMEN1, AMFR, METTL7B, TNFRSF17, HSP90AB1, VOPP1, GLT25D1, GALNT3, OMG, SIAE, FAR2, C7orf53, DNAJC13, HIST1H2BJ, KIAA0101, HSPB1, UBE2J1, HIST1H3J, CDS2, MGST3, PICALM, HINT1, SLC39A9, STOM, TRAF3, INSIG1, AP3B2, B3GAT3, CD300A, TGFBR1, HIST1H3A, PMAIP1, DSE, TMEM62, IGL@, IRF4, GSR, IRF1, EIF1AX, C9orf72, PMS2CL, C22orf37, FRMD3, IMP3, RPL17, FSD1L, APOLD1, B4GALT3, DCTN5, PPIF, CDC26, TAS2R31, RRP12, ANAPC11, GLDC, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference inSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference ipSIRS IRS biomarker profile, non-limiting examples of which include: HIST1H2AA, IFI16, PPP1R2, CCDC125, ZRANB1, SLC11A1, GPR56, 110, KDM6B, GAB2, CYP4F3, RGS2, KIAA1257, CPM, ACPL2, PRR13, ERGIC1, PTGDR, IRS2, MPZL3, AREG, SAP30, RBP7, CASS4, FKBP5, SYNE2, KLRD1, 132, KLRF1, LOC284757, HAL, TPST1, ECHDC3, CD163, CLEC4E, DAAM2, CHI3L1, MME, OCR1 (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference inSIRS IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference ipSIRS IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference ipSIRS IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference ipSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference healthy condition IRS biomarker profile, representative examples of which include: GNLY, GPR56, CHI3L1, KLRF1, KLRK1, PTGDR, SH2D1B, HIST1H2AJ, FAIM3, HLA-DPB1, CAMK4, FGFBP2, KLRD1, CLC, PHOSPHO1, HIST1H4C, ITK, LRRN3, CCR3, CRIP1, IMP3, HIST1H3J, HIST1H4E, HLA-DRA, PLA2G7, GIMAP7, HLA-DPA1, CASS4, HIST1H3I, KPNA5, CENPK, SULF2, KIAA1324, HIST1H3A, CFD, C1orf128, RPIA, MRPL41, GOT2, IL5RA, PYHIN1, ITGA4, HINT1, 200, VAMP2, C15orf54, LGALS2, 199, S100B, HSP90AB1, DNAJC9, PMAIP1, CCR4, RPL17, RUNX2, NPCDR1, JUP, PMS2CL, ANAPC11, PDE3B, RASA4, CAMK1D, LY6G5B, 268, FAM118A, PPIF, B4GALT3, B3GAT3, ABCG1, IRF4, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference ipSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed relative to a reference healthy condition IRS biomarker profile, illustrative examples of which include: ATP6V0D1, SAP30, GAB2, KRTAP15-1, NEK6, HDHD1A, SLC39A8, HIST1H2AA, FABP2, CDS2, SRXN1, KLHL5, ACPL2, HS2ST1, HIST1H2BJ, PLIN2, ICAM1, HSPB1, PRR13, P4HA1, SLC11A1, ECHDC3, TAF13, LGALS1, TAAR1, TPX2, DLEU2, TRIM21, AGTRAP, PTGS1, LHFP, CEP97, ACTA2, SIAE, GPR65, IL1RL1, MTHFS, FAM118B, MKI67, LRRFIP1, CCRL2, GALNT2, GSTO1, LRRC70, MTRR, ANKRD28, DPH3, 110, AIG1, UBE2F, LAIR1, PCOLCE2, PLB1, CDA, JKAMP, FRMD3, ITGA2B, SEC24A, RETN, THBS1, MYL9, SPARC, RCBTB2, PLAC8, PDK4, PPP2R5A, SH3PXD2B, DAAM2, NF-E4, DDAH2, MACF1, CD63, CLEC4A, MPO, SUCNR1, EXOSC4, EFCAB2, IL1B, OR9A2, AP3B2, DYNLL1, WSB2, SLC15A2, EAF2, C1orf161, TGFBR1, ABCA1, FFAR2, SLC1A3, ATP13A3, CLU, ADM, IFI16, KCNMA1, C9orf72, GSR, DSE, PICALM, EIF2AK2, HPSE, TLR10, HSPC159, TPST1, ODZ1, STOM, HMGB2, PDGFC, CCR1, OMG, CD163, SGMS2, TREML1, FOXD4L3, C7orf53, CEACAM6, FOLR3, METTL7B, TMEM144, DNAJC13, GALNT3, B3GNT5, CLEC4E, SLC37A3, ABCA13, CD24, C11orf82, FAR2, UBE2J1, GK3P, DEFA4, LTF, ACER3, TMTC1, SMPDL3A, FKBP5, ERLIN1, PLB1, MMP9, KREMEN1, ANKRD34B, OLAH, BMX, PFKFB2, HPGD, BPI, CD274, CEACAM8, TDRD9, LCN2, TNFAIP6, C3AR1, TCN1, IL18R1, CLEC4D, TLR5, RNASE2, FCGR1A, GPR84, OLFM4, VNN1, ARG1, CD177 (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference ipSIRS IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference healthy condition IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed, relative to a reference healthy condition IRS biomarker profile, as broadly described above.

Yet another subset of the disclosed IRS biomarkers has been identified as being useful for assisting in distinguishing between inSIRS affected subjects and ipSIRS affected subjects. Accordingly, in some embodiments, the methods and kits are useful for determining the likelihood that inSIRS or ipSIRS is present or absent in a subject. These methods and kits generally comprise or involve: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having inSIRS or ipSIRS, wherein the reference biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected from C11orf82, PLAC8, 132, INSIG1, CDS2, VOPP1, SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10, PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2, SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41, TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1, NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM, MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2, HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1, KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1, FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1, KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDC26, ICAM1, LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B, C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL, SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7, GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125, SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ, FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO, TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B, IFI16, CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3, ANKRD28, TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having inSIRS or ipSIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In illustrative examples of thus type, a reference inSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference ipSIRS IRS biomarker profile, non-limiting examples of which include: OLFM4, CD274, PLAC8, LCN2, IGJ, HIST1H4L, HIST1H3C, DEFA4, TDRD9, IGK@, HIST1H3B, CEACAM8, C11orf82, HIST1H2BM, LTF, HPGD, FOXD4L3, PDGFC, CD24, GPR84, CEACAM6, TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1, TLR10, DNAJC9, ABCA13, ANKRD34B, RNASE2, FCGR1A, HPSE, HIST1H3H, KIAA0746, ACER3, SDHC, MTRR, WSB2, CRIP1, IGLV6-57, ATP13A3, CD63, TREML1, PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, MPO, ITGA2B, LAIR1, PTGER2, EXOSC4, TYMS, LGALS1, C7orf58, SLC15A2, CD151, ADM, KREMEN1, RCBTB2, PTGS1, AMFR, ABCA1, METTL7B, TNFRSF17, DYNLL1, HSP90AB1, CLU, MKI67, VOPP1, UBE2F, P4HA1, GLT25D1, IL1B, SUCNR1, GALNT3, AIG1, CCR1, OMG, MACF1, CLEC4A, SIAE, FAR2, C7orf53, DNAJC13, HIST1H2BJ, JKAMP, KIAA0101, GSTO1, HSPB1, DDAH2, ICAM1, UBE2J1, KLHL5, HIST1H3J, EAF2, CDS2, MGST3, FFAR2, TPX2, PICALM, HINT1, SLC39A9, SEC24A, STOM, TRAF3, INSIG1, AP3B2, PCOLCE2, B3GAT3, TAF13, CD300A, TGFBR1, HIST1H3A, PMAIP1, AGTRAP, FAM118B, DSE, NEK6, CMTM5, GALNT2, TMEM62, HS2ST1, IGL@, ACTA2, LRRC70, IRF4, GSR, IRF1, EIF1AX, C9orf72, PMS2CL, ANKRD28, CTSL1, C22orf37, FRMD3, HDHD1A, CCRL2, IMP3, RPL17, FSD1L, APOLD1, B4GALT3, FSD1L, DCTN5, PPIF, CDC26, TAS2R31, RRP12, SFRS9, TAAR1, ANAPC11, SRXN1, GLDC, LASS4 (where if a gene name is not provided then a SEQ ID NO. is provided).

In other illustrative examples, a reference inSIRS IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference ipSIRS IRS biomarker profile, representative examples of which include: HIST1H2AA, LY6G5B, 268, IFI16, PPP1R2, CCDC125, ZRANB1, LOC100128751, SLC11A1, GPR56, RUNX2, 110, KDM6B, GAB2, 199, CYP4F3, RGS2, PDE3B, KIAA1257, CAMK1D, CPM, ACPL2, PRR13, ERGIC1, PTGDR, IRS2, MPZL3, MPZL2, AREG, SAP30, RBP7, CASS4, FKBP5, SYNE2, SULF2, KLRD1, 132, KLRF1, 314, LOC284757, HAL, TPST1, ECHDC3, CD163, KIAA1324, PLA2G7, CLEC4E, DAAM2, 200, CHI3L1, MME, OCR1 (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, an inSIRS IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference ipSIRS IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference ipSIRS IRS biomarker profile, as broadly described above.

Still another subset of the disclosed IRS biomarkers has been identified as being useful for assisting in distinguishing between subjects with different stages of ipSIRS selected from mild sepsis, severe sepsis and septic shock. Accordingly, in some embodiments, the methods and kits are useful for determining the likelihood that a stage of ipSIRS selected from mild sepsis, severe sepsis and septic shock is present or absent in a subject. These methods and kits generally comprise or involve: 1) providing a correlation of a reference IRS biomarker profile with the likelihood of having or not having the stage of ipSIRS, wherein the reference biomarker IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected from PLEKHA3, PLEKHF2, 232, SFRS9, ZNF587, KPNA5, LOC284757, GPR65, VAMP2, SLC1A3, ITK, ATF7, ZNF28, AIF1, MINPP1, GIMAP7, MKI67, IRF4, TSHZ2, HLA-DPB1, EFCAB2, POLE2, FAIM3, 110, CAMK4, TRIM21, IFI44, CENPK, ATPSL, GPR56, HLA-DPA1, C4orf3, GSR, GNLY, RFESD, BPI, HIST1H2AA, NF-E4, CALM2, EIF1AX, E2F6, ARL17P1, TLR5, SH3PXD2B, FAM118A, REIN, PMAIP1, DNAJC9, PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2, JKAMP, JUP, ABCG1, SLC39A9, B3GNT5, ACER3, LRRC70, NPCDR1, TYMS, HLA-DRA, TDRD9, FSD1L, FAR2, C7orf53, PPP1R2, SGMS2, EXOSC4, TGFBR1, CD24, TCN1, TAF13, AP3B2, CD63, SLC15A2, IL18R1, ATP6V0D1, SON, HSP90AB1, CEACAM8, SMPDL3A, IMP3, SEC24A, PICALM, 199, CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1, KIAA0746, KIAA1324, HINT1, VNN1, C22orf37, FSD1L, FOLR3, IL1RL1, OMG, MTHFS, OLFM4, S100B, ITGA4, KLRD1, SLC39A8, KLHL5, KLRK1, MPO, PPIF, GOT2, LRRN3, HIST1H2AJ, CLU, LCN2, 132, CEP97, KLRF1, FBXL13, HIST1H3B, ANKRD34B, RPIA, HPGD, HIST2H2BF, GK3P (where if a gene name is not provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker profile from the subject, which evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and (3) determining a likelihood of the subject having or not having the stage of ipSIRS based on the sample IRS biomarker profile and the reference IRS biomarker profile.

In illustrative examples of this type, a reference mild sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference severe sepsis IRS biomarker profile, illustrative examples of which include: OLFM4, CEACAM8, TCN1, BPI, LCN2, CD24, CEACAM6, NF-E4, HIST1H3B, MKI67, OLAH, TYMS, DNAJC9, MPO, LOC284757, ODZ1, HSP90AB1, VNN1, ANKRD34B, FBXL13, TSHZ2, KIAA0746, FOLR3, GSR, IRF4, LRRN3, TPX2, SFRS9, C7orf53, CYP4F3, IL1RL1, TDRD9, IL18R1, BMX, NPCDR1, GOT2, ATF7, CEP97, ITK, SEC24A, KIAA1324, FAM118A, 132, SMPDL3A, CD63, ABCG1, TLR5, CAMK4, CLU, SLC39A9, GK3P, LRRFIP1, AP3B2, SLC15A2, PICALM, HIST1H2AA, SGMS2, OMG, REIN, FAIM3, EXOSC4, SH3PXD2B, FAR2, 199, C4orf3, PCOLCE2 (where if a gene name is not provided then a SEQ ID NO. is provided).

In other illustrative examples, a reference mild sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference severe sepsis IRS biomarker profile, non-limiting examples of which include: JUP, SLC1A3, ECHDC3, IMP3, SLC39A8, MTHFS, TGFBR1, FSD1L, HIST2H2BF, HPGD, FSD1L, PPP1R2, B3GNT5, C22orf37, ACER3, GIMAP7, ATP6V0D1, KLHL5, PPIF, KLRK1, HINT1, GPR56, LRRC70, S100B, 110, SON, ZNF587, JKAMP, ITGA4, HLA-DRA, ZNF28, TRIM21, TAF13, HLA-DPA1, ARL17P1, KLRF1, PMAIP1, RPIA, ATPSL, VAMP2, E2F6, KLRD1, EIF1AX, PLEKHA3, GPR65, CENPK, CALM2, GNLY, DLEU2, HLA-DPB1, AIF1, KPNA5, EFCAB2, PLEKHF2, 232, RFESD, MINPP1, HIST1H2AJ, POLE2, IFI44 (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference mild sepsis IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference severe sepsis IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference severe sepsis IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference severe sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference septic shock IRS biomarker profile, non-limiting examples of which include: HPGD, SLC1A3, B3GNT5, SMPDL3A, ACER3, RETN, IL18R1, FSD1L, SH3PXD2B, SLC39A8, EXOSC4, FSD1L, AP3B2, ECHDC3, GPR65, TDRD9, BMX, PCOLCE2, PLEKHF2, SGMS2, RPIA, GK3P, FAR2, LRRC70, TGFBR1, MTHFS, C4orf3, TLR5, OLAH, TAF13, JKAMP, POLE2, PICALM, RFESD, ANKRD34B, OMG, VNN1, EIF1AX, KLHL5, SON, LRRFIP1, HIST1H2AJ, AIF1, SLC15A2, CALM2, CD63, HIST1H2AA, MINPP1, S100B, DLEU2, PLEKHA3, ODZ1, FOLR3, 232, EFCAB2, SEC24A, E2F6, SLC39A9, ZNF28, KLRF1, ATP6V0D1, IL1RL1, PPIF (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference severe sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference septic shock IRS biomarker profile, representative examples of which include: LCN2, CENPK, C22orf37, PMAIP1, KPNA5, ATP5L, TCN1, 132, CD24, ITGA4, KLRD1, SFRS9, TRIM21, VAMP2, GSR, LOC284757, PPP1R2, HINT1, 110, IMP3, C7orf53, ATF7, KIAA0746, GNLY, HLA-DRA, IFI44, ZNF587, CEP97, GPR56, OLFM4, CLU, KLRK1, GOT2, JUP, HLA-DPA1, NPCDR1, TPX2, HIST2H2BF, HLA-DPB1, FAM118A, ABCG1, MKI67, MPO, LRRN3, FBXL13, ARL17P1, CEACAM8, TSHZ2, 199, BPI, HSP90AB1, CYP4F3, TYMS, GIMAP7, DNAJC9, NF-E4, IRF4, HIST1H3B, CAMK4, FAIM3, CEACAM6, ITK, KIAA1324 (where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference severe sepsis IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference septic shock IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference septic shock IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference septic shock IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or underexpressed relative to a reference mild sepsis IRS biomarker profile, representative examples of which include: IFI44, HLA-DPB1, ARL17P1, HIST1H2AJ, MINPP1, GNLY, GIMAP7, HLA-DPA1, POLE2, 232, KPNA5, GPR56, HLA-DRA, ZNF587, KLRK1, RFESD, VAMP2, CENPK, KIAA1324, KLRD1, EFCAB2, ATPSL, 110, ITK, FAIM3, TRIM21, PMAIP1, HIST2H2BF, HINT1, DLEU2, AIF1, E2F6, ITGA4, KLRF1, CALM2, PLEKHA3, PPP1R2, CAMK4, 199, ZNF28, PLEKHF2, JUP, EIF1AX, PPIF, IMP3, C22orf37, ATP6V0D1, S100B, SON, GPR65, ABCG1, TAF13, FAM118A, RPIA, KLHL5, JKAMP, IRF4, CLU, CYP4F3, LRRC70 (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference septic shock IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relative to a reference mild sepsis IRS biomarker profile, illustrative examples of which include: GOT2, NPCDR1, CEP97, LRRN3, DNAJC9, TSHZ2, HSP90AB1, TYMS, HIST1H3B, ATF7, FBXL13, TPX2, TGFBR1, MPO, 132, NF-E4, MTHFS, CEACAM6, C7orf53, FSD1L, FSD1L, SLC39A9, MKI67, KIAA0746, HIST1H2AA, ACER3, ECHDC3, SLC15A2, SLC39A8, SEC24A, SFRS9, LRRFIP1, OMG, GSR, C4orf3, CD63, PICALM, LOC284757, FAR2, PCOLCE2, IL1RL1, B3GNT5, SGMS2, TLR5, EXOSC4, SH3PXD2B, GK3P, AP3B2, FOLR3, BPI, REIN, ODZ1, CEACAM8, BMX, HPGD, VNN1, ANKRD34B, SLC1A3, TDRD9, SMPDL3A, CD24, IL18R1, OLAH, LCN2, TCN1, OLFM4 (where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference septic shock IRS biomarker profile comprises: (1) at least one IRS biomarker that is downregulated or underexpressed relative to a reference mild sepsis IRS biomarker profile, as broadly described above and (2) at least one IRS biomarker that is upregulated or overexpressed relative to a reference mild sepsis IRS biomarker profile, as broadly described above.

In some embodiments, individual IRS biomarkers as broadly described above and elsewhere herein are selected from the group consisting of: (a) a polynucleotide expression product comprising a nucleotide sequence that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence identity with the sequence set forth in any one of SEQ ID NO: 1-319, or a complement thereof; (b) a polynucleotide expression product comprising a nucleotide sequence that encodes a polypeptide comprising the amino acid sequence set forth in any one of SEQ ID NO: 320-619; (c) a polynucleotide expression product comprising a nucleotide sequence that encodes a polypeptide that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence similarity or identity with at least a portion of the sequence set forth in SEQ ID NO: 320-619; (d) a polynucleotide expression product comprising a nucleotide sequence that hybridizes to the sequence of (a), (b), (c) or a complement thereof, under medium or high stringency conditions; (e) a polypeptide expression product comprising the amino acid sequence set forth in any one of SEQ ID NO: 320-619; and (f) a polypeptide expression product comprising an amino acid sequence that shares at least 70% (or at least 71% to at least 99% and all integer percentages in between) sequence similarity or identity with the sequence set forth in any one of SEQ ID NO: 320-619.

In some embodiments, the methods and kits comprise or involve: (1) measuring in the biological sample the level of an expression product of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarker gene and (2) comparing the measured level or functional activity of each expression product to the level or functional activity of a corresponding expression product in a reference sample.

The present invention also extends to the management of inSIRS, ipSIRS or particular stages of ipSIRS, or prevention of further progression of inSIRS, ipSIRS or particular stages of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), or assessment of the efficacy of therapies in subjects following positive diagnosis for the presence of inSIRS, ipSIRS or particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock) in a subject. The management of inSIRS or ipSIRS conditions is generally highly intensive and can include identification and amelioration of the underlying cause and aggressive use of therapeutic compounds such as, vasoactive compounds, antibiotics, steroids, antibodies to endotoxin, anti tumour necrosis factor agents, recombinant protein C. In addition, palliative therapies as described for example in Cohen and Glauser (1991, Lancet 338: 736-739) aimed at restoring and protecting organ function can be used such as intravenous fluids and oxygen and tight glycemic control. Therapies for ipSIRS are reviewed in Healy (2002, Ann. Pharmacother. 36(4): 648-54) and Brindley (2005, CJEM. 7(4): 227) and Jenkins (2006, J Hosp Med. 1(5): 285-295).

Typically, the therapeutic agents will be administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose. The dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time such as a reduction in, or relief from, the symptoms of inSIRS, ipSIRS or particular stages of ipSIRS. The quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner. In determining the effective amount of the active compound(s) to be administered in the treatment or prevention of inSIRS, ipSIRS or particular stages of ipSIRS, the medical practitioner or veterinarian may evaluate severity of any symptom associated with the presence of inSIRS, ipSIRS or particular stages of ipSIRS including, inflammation, blood pressure anomaly, tachycardia, tachypnea fever, chills, vomiting, diarrhoea, skin rash, headaches, confusion, muscle aches, seizures. In any event, those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.

The therapeutic agents may be administered in concert with adjunctive (palliative) therapies to increase oxygen supply to major organs, increase blood flow to major organs and/or to reduce the inflammatory response. Illustrative examples of such adjunctive therapies include non steroidal-anti inflammatory drugs (NSAIDs), intravenous saline and oxygen.

Thus, the present invention contemplates the use of the methods and kits described above and elsewhere herein in methods for treating, preventing or inhibiting the development of inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock) in a subject. These methods generally comprise (1) correlating a reference IRS biomarker profile with the presence or absence of a condition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker; (2) obtaining an IRS biomarker profile of a sample (i.e., “a sample IRS biomarker profile”) from a subject, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; (3) determining a likelihood of the subject having or not having the condition based on the sample IRS biomarker profile and the reference IRS biomarker profile, and administering to the subject, on the basis that the subject has an increased likelihood of having inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the subject has an increased likelihood of having ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.

In some embodiments the methods and kits of the present invention are used for monitoring, treatment and management of conditions that can lead to inSIRS or ipSIRS, illustrative examples of which include retained placenta, meningitis, endometriosis, shock, toxic shock (i.e., sequelae to tampon use), gastroenteritis, appendicitis, ulcerative colitis, Crohn's disease, inflammatory bowel disease, acid gut syndrome, liver failure and cirrhosis, failure of colostrum transfer in neonates, ischemia (in any organ), bacteraemia, infections within body cavities such as the peritoneal, pericardial, thecal, and pleural cavities, burns, severe wounds, excessive exercise or stress, haemodialysis, conditions involving intolerable pain (e.g., pancreatitis, kidney stones), surgical operations, and non-healing lesions. In these embodiments, the methods or kits of the present invention are typically used at a frequency that is effective to monitor the early development of inSIRS, ipSIRS or particular stages of ipSIRS, to thereby enable early therapeutic intervention and treatment of that condition. In illustrative examples, the diagnostic methods or kits are used at least at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hour intervals or at least 1, 2, 3, 4, 5 or 6 day intervals, or at least weekly, fortnightly or monthly. Accordingly, the present invention encompasses the use of the methods and kits of the present invention for early diagnosis of inSIRS, ipSIRS or particular stages of ipSIRS.

The term “early diagnosis” as used herein with “early detection” refers to specific screening/monitoring processes that allow detection and evaluation of inSIRS, ipSIRS or particular stages of ipSIRS at an early point in disease development and/or progression. For example, since both inSIRS and ipSIRS patients present with similar clinical signs, early detection of ipSIRS can be achieved through a plurality of evaluations of patients with inSIRS to detect a transition to ipSIRS.

The present invention can be practiced in the field of predictive medicine for the purposes of diagnosis or monitoring the presence or development of a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS in a subject, and/or monitoring response to therapy efficacy.

The IRS biomarker profile further enables determination of endpoints in pharmacotranslational studies. For example, clinical trials can take many months or even years to establish the pharmacological parameters for a medicament to be used in treating or preventing inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). However, these parameters may be associated with an IRS biomarker profile associated with a health state (e.g., a healthy condition). Hence, the clinical trial can be expedited by selecting a treatment regimen (e.g., medicament and pharmaceutical parameters), which results in an IRS biomarker profile associated with the desired health state (e.g., healthy condition). This may be determined for example by (1) providing a correlation of a reference IRS biomarker profile with the likelihood of having the healthy condition; (2) obtaining a corresponding IRS biomarker profile of a subject having inSIRS, ipSIRS or a particular stage of ipSIRS after treatment with a treatment regimen, wherein a similarity of the subject's IRS biomarker profile after treatment to the reference IRS biomarker profile indicates the likelihood that the treatment regimen is effective for changing the health status of the subject to the desired health state (e.g., healthy condition). This aspect of the present invention advantageously provides methods of monitoring the efficacy of a particular treatment regimen in a subject (for example, in the context of a clinical trial) already diagnosed with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS. These methods take advantage of IRS biomarkers that correlate with treatment efficacy, for example, to determine whether the IRS biomarker profile of a subject undergoing treatment partially or completely normalizes during the course of or following therapy or otherwise shows changes associated with responsiveness to the therapy.

The IRS biomarker profile further enables stratification of patients prior to enrolment in pharmacotranslational studies. For example, a clinical trial can be expedited by selecting a priori patients with a particular IRS biomarker profile that would most benefit from a particular treatment regimen (e.g., medicament and pharmaceutical parameters). For instance, patient enrolment into a clinical trial testing the efficacy of a new antibiotic would best include patients with an IRS biomarker profile that indicated that they had ipSIRS rather than inSIRS, and as such the selected patients would most likely benefit from the new therapy. Further, and by example, patient enrolment into a clinical trial testing the efficacy of a new inotrope would best include patients with an IRS biomarker profile that indicated that they had the shock stage of ipSIRS rather than inSIRS or other stage of ipSIRS, and as such the selected patients would most likely benefit from the new therapy.

As used herein, the term “treatment regimen” refers to prophylactic and/or therapeutic (i.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise. The term “treatment regimen” encompasses natural substances and pharmaceutical agents (i.e., “drugs”) as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, and combinations thereof.

Thus, the invention provides methods of correlating a reference IRS biomarker profile with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker. These methods generally comprise: (a) determining a sample IRS biomarker profile from a subject with the condition prior to treatment (i.e., baseline), wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker; and correlating the sample IRS biomarker profile with a treatment regimen that is effective for treating that condition.

The invention further provides methods of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). These methods generally comprise: (a) correlating a reference biomarker profile prior to treatment (i.e., baseline) with an effective treatment regimen for the condition, wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) obtaining a sample IRS biomarker profile from the subject after treatment, wherein the sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker, and wherein the sample IRS biomarker profile after treatment indicates whether the treatment regimen is effective for treating the condition in the subject.

The invention can also be practiced to evaluate whether a subject is responding (i.e., a positive response) or not responding (i.e., a negative response) to a treatment regimen. This aspect of the invention provides methods of correlating an IRS biomarker profile with a positive and/or negative response to a treatment regimen. These methods generally comprise: (a) obtaining an IRS biomarker profile from a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock) following commencement of the treatment regimen, wherein the IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) correlating the IRS biomarker profile from the subject with a positive and/or negative response to the treatment regimen.

The invention also provides methods of determining a positive and/or negative response to a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). These methods generally comprise: (a) correlating a reference IRS biomarker profile with a positive and/or negative response to the treatment regimen, wherein the reference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) determining a sample IRS biomarker profile from the subject, wherein the subject's sample IRS biomarker profile evaluates for an individual IRS biomarker in the reference IRS biomarker profile a corresponding IRS biomarker and indicates whether the subject is responding to the treatment regimen.

In some embodiments, the methods further comprise determining a first sample IRS biomarker profile from the subject prior to commencing the treatment regimen (i.e., a baseline profile), wherein the first sample IRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and comparing the first sample IRS biomarker profile with a second sample IRS biomarker profile from the subject after commencement of the treatment regimen, wherein the second sample IRS biomarker profile evaluates for an individual IRS biomarker in the first sample IRS biomarker profile a corresponding IRS biomarker.

This aspect of the invention can be practiced to identify responders or non-responders relatively early in the treatment process, i.e., before clinical manifestations of efficacy. In this way, the treatment regimen can optionally be discontinued, a different treatment protocol can be implemented and/or supplemental therapy can be administered. Thus, in some embodiments, a sample IRS biomarker profile is obtained within about 2 hours, 4 hours, 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, 12 weeks, 4 months, six months or longer of commencing therapy.

In order that the invention may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples.

EXAMPLES Example 1 Monitoring Severity of ipSIRS in Patients in Intensive Care

Patients admitted to intensive care (ICU) often have ipSIRS, or develop ipSIRS during their ICU stay. The ultimate aim of intensive care is to ensure the patient survives and is discharged to a general ward in the minimum time. Patients in intensive care with diagnosed ipSIRS are usually administered a number of therapeutic compounds—many of which have opposing actions on the immune system and many of which could be counterproductive depending on the severity of ipSIRS (mild sepsis, severe sepsis, septic shock). Monitoring intensive care patients on a regular basis with biomarkers of the present invention will allow medical practitioners to determine the stage of ipSIRS and hence choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers disclosed herein (“the IRS biomarkers”) will therefore allow medical practitioners to tailor and modify therapies to ensure patients survive and spend less time in intensive care. Less time in intensive care leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications. Practical examples of the use of the IRS biomarkers in Tables 1-6 are described.

Tables 1, 2 and 3 list those top 10 IRS biomarkers (by example) in ascending order of p value (less than 0.05) when comparing the clinical groups of mild sepsis, severe sepsis and septic shock (severe versus mild, shock versus mild and shock versus severe—the appropriate column is filled grey for each group in respective tables). In this and the following examples significance is defined when a p value is less than 0.05. P values were determined by adjusted t-test (Benjamini & Hochberg, 1995) in the case of healthy vs. other and inSIRS vs. ipSIRS, and by Tukey's Honestly Significant Difference for analysis of variance (ANOVA) for the mild/severe/shock comparisons. For the groups severe versus mild, shock versus mild and shock versus severe there were 72, 120 and 47 biomarkers respectively with a p value less than 0.05.

Tables 4, 5 and 6 list those top 10 biomarkers (by example) in descending order of Area Under Curve (AUC) value when comparing the clinical groups of mild sepsis, severe sepsis and septic shock (severe versus mild, shock versus mild and shock versus severe—the appropriate column is filled grey for each group in respective tables). For the groups severe versus mild, shock versus mild and shock versus severe there were 34, 17 and 2 biomarkers respectively with an AUC greater than 0.8 (a nominal cut-off above which would be considered to be good).

In each of Tables 1-6 a SEQ ID NO. is provided for each IRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), along with a database identification tag (e.g. NM_), a gene name (Gene Name) if there is one, and either; mean expression values for healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic shock and septic shock versus severe sepsis, or AUC values for HC vs. Sick, HC vs. inSIRS, HC vs. ipSIRS, inSIRS vs. ipSIRS, Mild Sepsis versus Severe Sepsis, Mild Sepsis versus Septic Shock and Septic Shock versus Severe Sepsis. Such biomarkers have clinical utility in determining ipSIRS severity based on these groups. By example, in Table 1, Severe versus Mild p Value, it can be seen that the gene PLEKHA3 has a significant p value for both Severe versus Mild and Shock versus Mild and therefore has utility in separating mild sepsis from both severe sepsis and septic shock. In Table 2, Severe versus Mild Area Under Curve, it can be seen that the gene PLEKHA3 has an AUC of 0.8748 and therefore has most utility in separating mild sepsis from severe sepsis. It can be seen that the p value for PLEKHA3 for separating septic shock from severe sepsis is not significant (>0.05) and therefore this biomarker has no utility in separating these two groups. From the columns in the table containing mean expression data it can be seen that PLEKHA3 is down-regulated in both severe sepsis (6.689) and septic shock (6.825) compared to mild sepsis (7.281) (also see FIG. 1).

Further and by example in Table 3, Shock vs. Mild p Value, it can be seen that the biomarker VAMP2 has utility in separating mild sepsis from septic shock but also from severe sepsis. VAMP2 does not have utility in separating septic shock from severe sepsis (p=0.708038) but does have further utility in separating healthy from other groups. From the mean expression columns it can also be seen that the expression level of VAMP2 is downregulated in both severe sepsis (8.454) and septic shock (8.353) compared to mild sepsis (9.016) (see also FIG. 2). In Table 4, Shock vs. Mild Area Under Curve, it can be seen that VAMP2 has an AUC of 0.8342.

Further and by example in Table 5, Shock versus Severe p Value, it can be seen that the biomarker ITK has utility in separating Shock versus Severe Sepsis and Mild Sepsis, and healthy from other groups but no utility in separating Severe Sepsis and Mild Sepsis. From the mean expression values for ITK it can be seen that it is comparatively downregulated in Septic Shock compared to both Severe and Mild Sepsis (see also FIG. 3). In Table 6, Shock versus Severe Area Under Curve, it can be seen that ITK has an AUC of 0.8054.

TABLE 1 Severe versus Mild p Value SEQ pval. pval. pval pval pval ID Database Gene HC inSIRS Mild Severe Shock HC.vs. inSIRS. Severe.vs. Shock.vs Shock.vs. Number ID Name Mean Mean Mean Mean Mean Other vs.ipSIRS Mild Mild Severe 285 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 587 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 232 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.000001 0.000000 0.869569 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.000001 0.000000 0.869569 195 NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 504 NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 288 NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 7.671 0.789194 1.000000 0.000012 0.023331 0.029137 590 NM 024613 PLEKHF2 7.432 7.660 8.044 7.255 7.671 0.789194 1.000000 0.000012 0.023331 0.029137 190 NM_176818 SFRS9 9.739 9.666 9.715 10.226 10.128 1.000000 0.000618 0.000018 0.000064 0.627141 499 NM_176818 SFRS9 9.739 9.666 9.715 10.226 10.128 1.000000 0.000618 0.000018 0.000064 0.627141 207 NR_002612 DLEU2 6.549 6.894 7.347 6.699 6.850 0.000171 1.000000 0.000019 0.000152 0.504529 NR_002612 DLEU2 6.549 6.894 7.347 6.699 6.850 0.000171 1.000000 0.000019 0.000152 0.504529 197 NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 506 NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 387 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 278 NM_001130 ATF7 5.212 5.212 5.253 5.605 5.372 1.000000 0.438013 0.000033 0.171523 0.007397 059 580 NM_001130 ATF7 5.212 5.212 5.253 5.605 5.372 1.000000 0.438013 0.000033 0.171523 0.007397 059 81 NR_046099 LOC284757 6.913 8.028 6.894 7.534 7.376 0.000001 0.000004 0.000039 0.000364 0.493398 NR_046099 LOC284757 6.913 8.028 6.894 7.534 7.376 0.000001 0.000004 0.000039 0.000364 0.493398

TABLE 2 Severe versus Mild Area Under Curve (AUC) SEQ HC vs. HC vs. HC vs. inSIRS vs. ID Database Gene Sick SIRS ipSIRS ipSIRS Mild vs. Mild vs. Severe vs. Number ID Name AUC AUC AUC AUC Severe AUC Shock AUC Shock AUC 195 NM_004897 MINPP1 0.689217443 0.699775533 0.685388685 0.532134532 0.88707483 0.779591837 0.488435374 504 NM_004897 MINPP1 0.689217443 0.699775533 0.685388685 0.532134532 0.88707483 0.779591837 0.488435374 285 NM_019091 PLEKHA3 0.584378734 0.644781145 0.562474562 0.541791542 0.874829932 0.794285714 0.619047619 587 NM_019091 PLEKHA3 0.584378734 0.644781145 0.562474562 0.541791542 0.874829932 0.794285714 0.619047619 282 NM_002692 POLE2 0.675029869 0.693602694 0.668294668 0.501165501 0.854421769 0.707755102 0.563265306 584 NM_002692 POLE2 0.675029869 0.693602694 0.668294668 0.501165501 0.854421769 0.707755102 0.563265306 288 NM_024613 PLEKHF2 0.654868578 0.667227834 0.65038665 0.538794539 0.850340136 0.692244898 0.718367347 590 NM_024613 PLEKHF2 0.654868578 0.667227834 0.65038665 0.538794539 0.850340136 0.692244898 0.718367347 190 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102 499 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102 170 NM_003608 GPR65 0.765681004 0.735690236 0.776556777 0.6003996 0.839455782 0.615510204 0.740136054 480 NM 003608 GPR65 0.765681004 0.735690236 0.776556777 0.6003996 0.839455782 0.615510204 0.740136054 232 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.661338661 0.838095238 0.83755102 0.559183673 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.661338661 0.838095238 0.83755102 0.559183673 197 NM_006969 ZNF28 0.689964158 0.837261504 0.636548637 0.670662671 0.8367346941 0.796734694 0.536054422 506 NM_006969 ZNF28 0.689964158 0.837261504 0.636548637 0.670662671 0.836734694 0.796734694 0.536054422 81 NR_046099 LOC284757 0.738948626 0.939393939 0.666259666 0.835497835 0.834013605 0.749387755 0.613605442 NR_046099 LOC284757 0.738948626 0.939393939 0.666259666 0.835497835 0.834013605 0.749387755 0.613605442 71 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234 0.564768565 0.831292517 0.844897959 0.530612245 387 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234 0.564768565 0.831292517 0.844897959 0.530612245

TABLE 3 Shock vs. Mild p Value SEQ pval. pval. pval pval pval ID Database Gene HC inSIRS Mild Severe Shock HC.vs. inSIRS. Severe.vs. Shock.vs. Shock.vs. Number ID Name Mean Mean Mean Mean Mean Other vs.IpSIRS Mild Mild Severe 59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 8.353 0.000000 0.297018 0.000084 0.000000 0.708038 376 NM_014232 VAMP2 9.213 8.896 9.016 8.454 8.353 0.000000 0.297018 0.000084 0.000000 0.708038 71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 387 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 232 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.000001 0.000000 0.869569 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.000001 0.000000 0.869569 246 NM_032828 ZNF587 8.514 8.816 8.783 8.381 8.101 1.000000 0.003242 0.018153 0.000001 0.136854 549 NM 032828 ZNF587 8.514 8.816 8.783 8.381 8.101 1.000000 0.003242 0.018153 0.000001 0.136854 195 NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 504 NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 107 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 8.112 0.000000 1.000000 0.310769 0.000008 0.014285 420 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 8.112 0.000000 1.000000 0.310769 0.000008 0.014285 285 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 587 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 58 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971 9.578 0.000000 0.067333 0.026428 0.000013 0.253098 375 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971 9.578 0.000000 0.067333 0.026428 0.000013 0.253098 304 NR_033759 ATP5L 7.242 7.337 7.379 6.824 6.772 1.000000 0.748516 0.000612 0.000014 0.929559 NR_033759 ATP5L 7.242 7.337 7.379 6.824 6.772 1.000000 0.748516 0.000612 0.000014 0.929559 197 NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 506 NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.000020 0.000018 0.814925

TABLE 4 Shock vs. Mild Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number Database ID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 71 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234 0.564768565 0.831292517 0.844897959 0.530612245 387 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234 0.564768565 0.831292517 0.844897959 0.530612245 246 NM_032828 ZNF587 0.506571087 0.632435466 0.539072039 0.68964369 0.723809524 0.84244898 0.644897959 549 NM_032828 ZNF587 0.506571087 0.632435466 0.539072039 0.68964369 0.723809524 0.84244898 0.644897959 232 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.661338661 0.838095238 0.83755102 0.559183673 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.661338661 0.838095238 0.83755102 0.559183673 59 NM_014232 VAMP2 0.814366786 0.78338945 0.825600326 0.636363636 0.810884354 0.834285714 0.54829932 376 NM_014232 VAMP2 0.814366786 0.78338945 0.825600326 0.636363636 0.810884354 0.834285714 0.54829932 107 NM_153236 GIMAP7 0.829898447 0.80359147 0.839438339 0.603063603 0.617687075 0.826938776 0.710204082 420 NM_153236 GIMAP7 0.829898447 0.80359147 0.839438339 0.603063603 0.617687075 0.826938776 0.710204082 58 NM_002121 HLA-DPB1 0.857228196 0.814814815 0.872608873 0.682317682 0.693877551 0.809795918 0.628571429 375 NM_002121 HLA-DPB1 0.857228196 0.814814815 0.872608873 0.682317682 0.693877551 0.809795918 0.628571429 110 gi|13182974 NA 0.796893668 0.92704826 0.74969475 0.743589744 0.681632653 0.808163265 0.617687075 423 gi|13182974 NA 0.796893668 0.92704826 0.74969475 0.743589744 0.681632653 0.808163265 0.617687075 64 NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 381 NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 190 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102 499 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102 197 NM_006969 ZNF28 0.689964158 0.837261504 0.636548637 0.670662671 0.836734694 0.796734694 0.536054422 506 NM_006969 ZNF28 0.689964158 0.837261504 0.636548637 0.670662671 0.836734694 0.796734694 0.536054422

TABLE 5 Shock versus Severe p Value pval pval pval SEQ ID Gene inSIRS Mild Severe Shock pval.HC.vs. pval.inSIRS.vs. Severe.vs. Shock.vs. Shock.vs. Number Database ID Name HC Mean Mean Mean Mean Mean Other Sepsis Mild Mild Severe 79 NM_005546 ITK 9.271 8.099 8.227 8.536 7.635 0.000000 1.000000 0.276607 0.002832 0.000063 395 NM_005546 ITK 9.271 8.099 8.227 8.536 7.635 0.000000 1.000000 0.276607 0.002832 0.000063 34 NM_001744 CAMK4 8.155 6.723 6.902 7.152 6.470 0.000000 1.000000 0.305982 0.011176 0.000339 352 NM_001744 CAMK4 8.155 6.723 6.902 7.152 6.470 0.000000 1.000000 0.305982 0.011176 0.000339 64 NM_004172 SLC1A3 5.849 6.892 6.472 6.447 7.373 0.000000 1.000000 0.993673 0.000056 0.000352 381 NM_004172 SLC1A3 5.849 6.892 6.472 6.447 7.373 0.000000 1.000000 0.993673 0.000056 0.000352 171 NR_046000 IRF4 8.200 7.491 7.843 8.381 7.762 0.000105 0.000070 0.002770 0.825556 0.000501 NR_046000 IRF4 8.200 7.491 7.843 8.381 7.762 0.000105 0.000070 0.002770 0.825556 0.000501 271 NM_173485 TSHZ2 7.382 6.972 6.846 7.429 6.929 0.004682 1.000000 0.000051 0.739871 0.000534 574 NM_173485 TSHZ2 7.382 6.972 6.846 7.429 6.929 0.004682 1.000000 0.000051 0.739871 0.000534 22 NM_005449 FAIM3 10.259 9.101 9.036 9.174 8.464 0.000000 1.000000 0.732360 0.001453 0.000582 340 NM_005449 FAIM3 10.259 9.101 9.036 9.174 8.464 0.000000 1.000000 0.732360 0.001453 0.000582 44 NM_032047 B3GNT5 6.871 8.033 8.009 7.744 8.548 0.000000 1.000000 0.438680 0.013149 0.000955 362 NM_032047 B3GNT5 6.871 8.033 8.009 7.744 8.548 0.000000 1.000000 0.438680 0.013149 0.000955 229 NM_207647 FSD1L 4.605 4.402 4.801 4.565 5.099 1.000000 0.000000 0.235142 0.050177 0.001077 534 NM_207647 FSD1L 4.605 4.402 4.801 4.565 5.099 1.000000 0.000000 0.235142 0.050177 0.001077 198 gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 4.817 0.000001 1.000000 0.000589 0.919120 0.001847 507 gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 4.817 0.000001 1.000000 0.000589 0.919120 0.001847 220 NM_207627 ABCG1 8.318 7.923 7.960 8.214 7.791 0.000000 1.000000 0.112606 0.270961 0.003184 526 NM_207627 ABCG1 8.318 7.923 7.960 8.214 7.791 0.000000 1.000000 0.112606 0.270961 0.003184

TABLE 6 Shock versus Severe Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number Database ID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 64 NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 381 NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 79 NM_005546 ITK 0.888888889 0.873737374 0.894383394 0.520812521 0.646258503 0.735510204 0.805442177 395 NM_005546 ITK 0.888888889 0.873737374 0.894383394 0.520812521 0.646258503 0.735510204 0.805442177 22 NM_005449 FAIM3 0.951612903 0.930415264 0.959299959 0.596070596 0.57414966 0.749387755 0.793197279 340 NM_005449 FAIM3 0.951612903 0.930415264 0.959299959 0.596070596 0.57414966 0.749387755 0.793197279 171 NR_046000 IRF4 0.776433692 0.946127946 0.714896215 0.741258741 0.759183673 0.604897959 0.782312925 NR_046000 IRF4 0.776433692 0.946127946 0.714896215 0.741258741 0.759183673 0.604897959 0.782312925 34 NM_001744 CAMK4 0.939217443 0.921436588 0.945665446 0.55977356 0.594557823 0.725714286 0.776870748 352 NM_001744 CAMK4 0.939217443 0.921436588 0.945665446 0.55977356 0.594557823 0.725714286 0.776870748 271 NM_173485 TSHZ2 0.705197133 0.733445567 0.694953195 0.543456543 0.828571429 0.528979592 0.776870748 574 NM_173485 TSHZ2 0.705197133 0.733445567 0.694953195 0.543456543 0.828571429 0.528979592 0.776870748 88 NM_181506 LRRC70 0.795997611 0.49382716 0.901098901 0.857808858 0.736054422 0.515102041 0.76462585 402 NM_181506 LRRC70 0.795997611 0.49382716 0.901098901 0.857808858 0.736054422 0.515102041 0.76462585 176 NM_002230 JUP 0.830346476 0.75308642 0.858363858 0.573093573 0.500680272 0.765714286 0.760544218 485 NM_002230 JUP 0.830346476 0.75308642 0.858363858 0.573093573 0.500680272 0.765714286 0.760544218 44 NM_032047 B3GNT5 0.9369773 0.948933782 0.932641433 0.548784549 0.565986395 0.692244898 0.759183673 362 NM_032047 B3GNT5 0.9369773 0.948933782 0.932641433 0.548784549 0.565986395 0.692244898 0.759183673 235 NM_003531 HIST1H3C 0.781212664 0.881593715 0.744810745 0.672327672 0.643537415 0.651428571 0.752380952 539 NM_003531 HIST1H3C 0.781212664 0.881593715 0.744810745 0.672327672 0.643537415 0.651428571 0.752380952

Example 2 Differentiating inSIRS and ipSIRS in Post-Surgical and Medical Patients

Surgical and medical patients often develop inSIRS post-surgery, post-procedural or as part of a co-morbidity or co-morbidities. Such inpatients have a higher incidence of inSIRS and a higher risk of developing ipSIRS. Medical care in such patients therefore involves monitoring for signs of inSIRS and ipSIRS, differentiating between these two conditions, and determining at the earliest possible time when a patient transitions from inSIRS to ipSIRS. The treatment and management of inSIRS and ipSIRS patients is different, since inSIRS patients can be put on mild anti-inflammatory drugs or anti-pyretics and ipSIRS patients must be started on antibiotics as soon as possible for best outcomes. Monitoring post-surgical and medical patients on a regular basis with biomarkers of the present invention will allow nursing and medical practitioners to differentiate inSIRS and ipSIRS at an early stage and hence make informed decisions on choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers will therefore allow medical practitioners to tailor and modify therapies to ensure patients recover quickly from surgery and do not develop ipSIRS. Less time in hospital and less complications leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications. Practical examples of the use of the biomarkers in Tables 7 and 8 are described.

Table 7 lists the top 10 biomarkers (of 216) in order of ascending p value when comparing the two clinical groups of inSIRS and ipSIRS. A SEQ ID NO. is provided for each IRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), along with a database identification tag (e.g. NM_), a gene name (Gene Name) if there is one, mean expression values for healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic shock and septic shock versus severe sepsis. All biomarkers have clinical utility in distinguishing inSIRS and ipSIRS and for distinguishing inSIRS and ipSIRS as early as possible. Seven (7) of these biomarkers are also useful in distinguishing healthy control from sick although this has no clinical utility for post-surgical or medical patients. Some of these biomarkers also have limited utility in determining ipSIRS severity as indicated by respective p values less than 0.05. By example, in Table 7, inSIRS vs. ipSIRS p Value, it can be seen that the gene C11orf82 has a significant p value for both inSIRS versus ipSIRS and Healthy versus other groups and therefore has utility in separating healthy and inSIRS patients from septic patients. From the columns in the table containing mean expression data it can be seen that C11orf82 is down-regulated in both inSIRS (5.888) and healthy controls (5.776) compared to septic patients of all classes (mild (6.889), severe (7.153) and shock (7.293)) (7.281) (also see FIG. 4).

Table 8 lists the top 10 biomarkers (of 104 with an AUC>0.8) in order of descending AUC when comparing the two clinical groups of inSIRS and ipSIRS and it can be seen that C11orf82, PLAC8 and INSIG1 have AUCs of 0.9477, 0.9210 and 0.9120, respectively (see also FIGS. 4, 5 and 6).

TABLE 7 inSIRS VERSUS ipSIRS p Value pval pval HC pval pval Shock SEQ ID Gene HC inSIRS Mild Severe Shock vs. pval inSIRS Severe Shock vs. Number Database ID Name Mean Mean Mean Mean Mean Other vs. ipSIRS vs. Mild vs. Mild Severe 12 NM_145018 C11orf82 5.776 5.888 6.889 7.153 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 330 NM_145018 C11orf82 5.776 5.888 6.889 7.153 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 83 NR_036641 PDGFC 6.098 6.117 6.987 7.044 7.466 0.000000 0.000000 0.970637 0.064634 0.196970 NR_036641 PDGFC 6.098 6.117 6.987 7.044 7.466 0.000000 0.000000 0.970637 0.064634 0.196970 106 NM_018375 SLC39A9 8.038 7.719 8.121 8.368 8.428 1.000000 0.000000 0.034062 0.001276 0.808136 419 NM_018375 SLC39A9 8.038 7.719 8.121 8.368 8.428 1.000000 0.000000 0.034062 0.001276 0.808136 150 NM_030796 VOPP1 9.302 8.771 9.510 9.318 9.517 1.000000 0.000000 0.298375 0.997162 0.269787 461 NM_030796 VOPP1 9.302 8.771 9.510 9.318 9.517 1.000000 0.000000 0.298375 0.997162 0.269787 73 NM_001257400 CD63 9.235 9.126 9.718 9.990 10.159 0.000000 0.000000 0.156468 0.002260 0.485665 389 NM_001257400 CD63 9.235 9.126 9.718 9.990 10.159 0.000000 0.000000 0.156468 0.002260 0.485665 55 NM_014143 CD274 5.508 5.656 7.557 7.211 7.237 0.000000 0.000000 0.536662 0.490374 0.996684 372 NM_014143 CD274 5.508 5.656 7.557 7.211 7.237 0.000000 0.000000 0.536662 0.490374 0.996684 111 NM_198336 INSIG1 8.081 7.370 8.062 7.867 7.913 0.001237 0.000000 0.123875 0.197540 0.883915 424 NM_198336 INSIG1 8.081 7.370 8.062 7.867 7.913 0.001237 0.000000 0.123875 0.197540 0.883915 76 ENST00000443533 DDAH2 8.067 8.170 8.630 8.707 9.015 0.000000 0.000000 0.868573 0.011535 0.108528 392 ENST00000443533 DDAH2 8.067 8.170 8.630 8.707 9.015 0.000000 0.000000 0.868573 0.011535 0.108528 115 NM_003546 HIST1H4L 9.807 7.908 9.466 9.602 9.065 0.000032 0.000000 0.878290 0.231084 0.140998 428 NM_003546 HIST1H4L 9.807 7.908 9.466 9.602 9.065 0.000032 0.000000 0.878290 0.231084 0.140998 226 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501 8.852 1.000000 0.000000 0.042709 0.908040 0.098544 532 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501 8.852 1.000000 0.000000 0.042709 0.908040 0.098544

TABLE 8 inSIRS versus ipSIRS Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number Database ID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 12 NM_145018 C11orf82 0.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.650612245 0.555102041 330 NM_145018 C11orf82 0.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.650612245 0.555102041 72 NM_001130715 PLAC8 0.635902031 0.828282828 0.804232804 0.921078921 0.506122449 0.653061224 0.642176871 388 NM_001130715 PLAC8 0.635902031 0.828282828 0.804232804 0.921078921 0.506122449 0.653061224 0.642176871 132 gi|21757933 NA 0.533004779 0.867564534 0.588319088 0.912753913 0.708843537 0.631020408 0.540136054 445 gi|21757933 NA 0.533004779 0.867564534 0.588319088 0.912753913 0.708843537 0.631020408 0.540136054 111 NM_198336 INSIG1 0.695191159 0.957351291 0.6001221 0.912087912 0.666666667 0.631836735 0.518367347 424 NM_198336 INSIG1 0.695191159 0.957351291 0.6001221 0.912087912 0.666666667 0.631836735 0.518367347 90 gi|21749325 CDS2 0.669354839 0.730078563 0.814204314 0.907092907 0.586394558 0.56244898 0.504761905 gi|21749325 CDS2 0.669354839 0.730078563 0.814204314 0.907092907 0.586394558 0.56244898 0.504761905 150 NM_030796 VOPP1 0.53875448 0.937710438 0.605921856 0.906759907 0.63537415 0.544489796 0.66122449 461 NM_030796 VOPP1 0.53875448 0.937710438 0.605921856 0.906759907 0.63537415 0.544489796 0.66122449 106 NM_018375 SLC39A9 0.559587814 0.775533109 0.681115181 0.901098901 0.730612245 0.735510204 0.557823129 419 NM_018375 SLC39A9 0.559587814 0.775533109 0.681115181 0.901098901 0.730612245 0.735510204 0.557823129 37 NM_199135 FOXD4L3 0.815860215 0.49382716 0.928164428 0.900765901 0.597278912 0.608163265 0.48707483 355 NM_199135 FOXD4L3 0.815860215 0.49382716 0.928164428 0.900765901 0.597278912 0.608163265 0.48707483 68 NM_018639 WSB2 0.782108722 0.581369248 0.913919414 0.9004329 0.555102041 0.533877551 0.530612245 384 NM_018639 WSB2 0.782108722 0.581369248 0.913919414 0.9004329 0.555102041 0.533877551 0.530612245 73 NM_001257400 CD63 0.73655914 0.612233446 0.863044363 0.897768898 0.644897959 0.72244898 0.613605442 389 NM_001257400 CD63 0.73655914 0.612233446 0.863044363 0.897768898 0.644897959 0.72244898 0.613605442

Example 3 Differentiating Both inSIRS and ipSIRS in Emergency Department Patients and Determining Degree of Illness

Patients presenting to emergency departments often have a fever, which is one (of four) of the clinical signs of inSIRS. Such patients need to be assessed to determine if they have either inSIRS or ipSIRS. Further it is important to determine how sick they are to be able to make a judgement call on whether to admit the patient or not. As mentioned above, the treatment and management of pyretic, inSIRS and septic patients are different. By way of example, a patient with a fever without other inSIRS clinical signs and no obvious source of infection may be sent home, or provided with other non-hospital services, without further hospital treatment. However, a patient with a fever may have early ipSIRS and not admitting such a patient may put their life at risk. Because these biomarkers can differentiate inSIRS and ipSIRS and determine how sick a patient is they will allow medical practitioners to triage emergency department patients quickly and effectively. Accurate triage decision-making insures that patients requiring hospital treatment are given it, and those that don't are provided with other appropriate services. Practical examples of the use of the biomarkers in Tables 9 and 10 are described.

Table 9 lists 30 significant biomarkers when comparing the groups of healthy and sick (sick consisting of those patients with either inSIRS or ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is provided for each IRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), along with a database identification tag (e.g. NM_), a gene name (Gene Name) if there is one, mean expression values for healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic shock and septic shock versus severe sepsis. Such biomarkers have clinical utility in distinguishing healthy from sick patients and inSIRS from ipSIRS patients. By example, in Table 9, Healthy versus inSIRS versus ipSIRS, it can be seen that the gene FCGR1A has a significant p value for both inSIRS versus ipSIRS and Healthy versus other groups and therefore has utility in separating healthy and inSIRS and ipSIRS patients. From the columns in the table containing mean expression data it can be seen that FCGR1A is up-regulated in inSIRS (9.281) compared to healthy controls (7.871) but more so in ipSIRS patients (9.985-10.308). Such a upward gradient in gene expression can be used to determine the degree of illness in patients presenting to an emergency department allowing clinicians to risk stratify and triage with greater certainty (see also FIG. 7).

Table 10 lists 10 significant biomarkers when comparing the groups of healthy and sick (sick consisting of those patients with either inSIRS or ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is provided for each IRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), along with a database identification tag (e.g. NM_), a gene name (Gene Name) if there is one, mean expression values for healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic shock and septic shock versus severe sepsis. Such biomarkers have clinical utility in distinguishing healthy from sick patients and inSIRS from ipSIRS patients. By example, in Table 10, Healthy versus inSIRS versus ipSIRS, it can be seen that the gene CHI3L1 has a significant p value for both inSIRS versus ipSIRS and Healthy versus other groups and therefore has utility in separating healthy and inSIRS and septic patients. From the columns in the table containing mean expression data it can be seen that CHI3L1 is down-regulated in inSIRS (9.876) compared to healthy controls (10.47) but more so in ipSIRS patients (8.64-9.035). Such a downward gradient in gene expression can be used to determine the degree of illness in patients presenting to an emergency department allowing clinicians to risk stratify and triage with greater certainty (see also FIG. 8).

TABLE 9 Healthy versus inSIRS versus ipSIRS p Value pval pval pval pval SEQ ID inSIRS Mild Severe Shock pval HC inSIRS vs. Severe vs. Shock vs. Shock vs. Number Database ID Gene Name HC Mean Mean Mean Mean Mean vs. Other ipSIRS Mild Mild Severe 79 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 395 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 34 NM_153046 TDRD9 4.986 5.567 6.483 6.937 7.385 0.000000 0.000000 0.248195 0.001153 0.259068 352 NM_153046 TDRD9 4.986 5.567 6.483 6.937 7.385 0.000000 0.000000 0.248195 0.001153 0.259068 64 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 381 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 171 NM_018367 ACER3 7.317 7.845 8.701 8.417 9.050 0.000000 0.000000 0.362961 0.132905 0.008450 NM_018367 ACER3 7.317 7.845 8.701 8.417 9.050 0.000000 0.000000 0.362961 0.132905 0.008450 271 NM_000860 HPGD 5.621 6.238 7.085 6.908 7.946 0.000000 0.000025 0.895298 0.035905 0.027021 574 NM_000860 HPGD 5.621 6.238 7.085 6.908 7.946 0.000000 0.000025 0.895298 0.035905 0.027021 22 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.000000 0.000069 0.003150 0.006691 0.784808 340 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.000000 0.000069 0.003150 0.006691 0.784808 44 NM_004054 C3AR1 8.429 9.449 10.261 10.439 10.593 0.000000 0.000016 0.650271 0.142678 0.725241 362 NM_004054 C3AR1 8.429 9.449 10.261 10.439 10.593 0.000000 0.000016 0.650271 0.142678 0.725241 229 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.690976 0.351809 534 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.690976 0.351809 198 NM_032045 KREMEN1 8.626 9.409 10.143 10.189 10.055 0.000000 0.000079 0.962640 0.837239 0.731337 507 NM_032045 KREMEN1 8.626 9.409 10.143 10.189 10.055 0.000000 0.000079 0.962640 0.837239 0.731337 220 NM_003268 TLR5 7.747 9.010 9.726 9.979 10.311 0.000000 0.000225 0.275728 0.000244 0.110996 526 NM_003268 TLR5 7.747 9.010 9.726 9.979 10.311 0.000000 0.000225 0.275728 0.000244 0.110996 281 NM_153021 PLB1 8.205 8.887 9.574 9.463 10.019 0.000000 0.000133 0.872838 0.059398 0.037699 583 NM_153021 PLB1 8.205 8.887 9.574 9.463 10.019 0.000000 0.000133 0.872838 0.059398 0.037699 214 NM_004482 GALNT3 5.685 6.251 6.916 6.728 7.075 0.000000 0.000000 0.407446 0.422801 0.051201 520 NM_004482 GALNT3 5.685 6.251 6.916 6.728 7.075 0.000000 0.000000 0.407446 0.422801 0.051201 27 NM_001816 CEACAM8 7.336 7.874 8.503 9.775 9.287 0.000000 0.001298 0.011921 0.098854 0.501991 345 NM_001816 CEACAM8 7.336 7.874 8.503 9.775 9.287 0.000000 0.001298 0.011921 0.098854 0.501991 242 NM_007115 TNFAIP6 7.738 9.246 9.829 9.631 9.738 0.000000 1.000000 0.712067 0.908467 0.905260 545 NM_007115 TNFAIP6 7.738 9.246 9.829 9.631 9.738 0.000000 1.000000 0.712067 0.908467 0.905260 170 NM_016021 UBE2J1 8.792 9.555 10.118 10.044 10.335 0.000000 0.000015 0.817659 0.104460 0.049488 480 NM_016021 UBE2J1 8.792 9.555 10.118 10.044 10.335 0.000000 0.000015 0.817659 0.104460 0.049488 278 NM_015268 DNAJC13 7.507 8.083 8.596 8.693 8.878 0.000000 0.000000 0.833718 0.133216 0.512817 580 NM_015268 DNAJC13 7.507 8.083 8.596 8.693 8.878 0.000000 0.000000 0.833718 0.133216 0.512817

TABLE 10 Healthy versus inSIRS versus ipSIRS p Value SEQ ID pval pval pval pval Num- HC inSIRS Mild Severe Shock pval HC inSIRS Severe vs. Shock Shock vs. ber Database ID Gene Name Mean Mean Mean Mean Mean vs. Other vs. ipSIRS Mild vs. Mild Severe 104 NM_001276 CHI3L1 10.470 9.876 8.640 9.035 8.726 0.000000 0.000056 0.485576 0.954853 0.641602 417 NM_001276 CHI3L1 10.470 9.876 8.640 9.035 8.726 0.000000 0.000056 0.485576 0.954853 0.641602 122 NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 10.185 0.000000 0.048380 0.088690 0.661703 0.354179 435 NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 10.185 0.000000 0.048380 0.088690 0.661703 0.354179 40 NM_016523 KLRF1 6.343 5.438 5.022 4.504 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 358 NM_016523 KLRF1 6.343 5.438 5.022 4.504 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 33 NM_000953 PTGDR 9.310 8.373 8.028 7.790 7.577 0.000000 0.007043 0.500548 0.040553 0.570947 351 NM_000953 PTGDR 9.310 8.373 8.028 7.790 7.577 0.000000 0.007043 0.500548 0.040553 0.570947 103 ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 7.123 0.000000 0.011838 0.056985 0.008125 0.944270 416 ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 7.123 0.000000 0.011838 0.056985 0.008125 0.944270

Example 4 Differentiating Healthy from Sick Patients and Determining Degree of Illness

Patients presenting to medical clinics often have any one of the four clinical signs of inSIRS (increased heart rate, increased respiratory rate, abnormal white blood cell count, fever or hypothermia). Many different clinical conditions can present with one of the four clinical signs of inSIRS and such patients need to be assessed to determine if they have either inSIRS or ipSIRS and to exclude other differential diagnoses. By way of example, a patient with colic might also present with clinical signs of increased heart rate. Differential diagnoses could be (but not limited to) appendicitis, urolithiasis, cholecystitis, pancreatitis, enterocolitis. In each of these conditions it would be important to determine if there was a systemic inflammatory response (inSIRS) or whether an infection was contributing to the condition. The treatment and management of patients with and without systemic inflammation and/or infection are different. Because these biomarkers can differentiate healthy from sick (inSIRS and ipSIRS), and determine the degree of systemic involvement, the use of them will allow medical practitioners to determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue. Practical examples of the use of the biomarkers in Tables 11, 12, 13 and 14 are described.

Table 11 lists 20 significant biomarkers (of 150) when comparing the groups of healthy and sick (sick consisting of those patients with either inSIRS or ipSIRS). A SEQ ID NO. is provided for each IRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), along with a database identification tag (e.g. NM_), a gene name (Gene Name) if there is one, mean expression values for healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic shock and septic shock versus severe sepsis. Such biomarkers have clinical utility in distinguishing healthy from sick patients and determining the level of systemic inflammation and/or infection. For example, in Table 11, Healthy versus Sick, it can be seen that the gene CD177 has a significant p value for healthy control versus other groups and therefore has utility in separating healthy and sick patients. From the columns in the table containing mean expression data it can be seen that CD177 is up-regulated in inSIRS (10.809) compared to healthy controls (8.091) but more so in ipSIRS patients (11.267-12.088). Such up-regulated differences in gene expression can be used to determine the degree of systemic inflammation and infection in patients presenting to clinics allowing clinicians to more easily determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue (see also FIG. 9).

Further, and by example, in Table 11, Healthy versus Sick, it can be seen that the gene GNLY has a significant p value for healthy control versus other groups and therefore has utility in separating healthy and sick patients. From the columns in the table containing mean expression data it can be seen that GNLY is down-regulated in inSIRS (9.428) compared to healthy controls (10.653) but more so in septic patients (9.305-8.408). GNLY has an AUC of 0.9445 (not shown) for separating healthy and sick patients. Such down-regulated differences in gene expression can be used to determine the degree of systemic inflammation and infection in patients presenting to clinics allowing clinicians to more easily determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue (see also FIG. 10).

Table 12 lists the top 10 biomarkers (of 118 with an AUC of at least 0.8) for separating healthy from sick patients (sick being those patients with either inSIRS or ipSIRS) by decreasing value of Area Under Curve (AUC). It can be seen that the highest AUC is for CD177 for separating healthy from sick (0.9929) (see also FIG. 9).

Table 13 lists the top 10 biomarkers (of 152 with an AUC of at least 0.8) for separating healthy from inSIRS patients by decreasing value of Area Under Curve (AUC). It can be seen that the highest AUC is for BMX for separating healthy from inSIRS (1). That is, this biomarker alone can perfectly separate these two groups (see also FIG. 11).

Table 14 lists the top 10 biomarkers (of 140 with an AUC of at least 0.8) for separating healthy from ipSIRS patients by decreasing value of Area Under Curve (AUC). It can be seen that the highest AUC is for TLR5 for separating healthy from ipSIRS (0.9945) (see also FIG. 12).

TABLE 11 Healthy versus Sick p Value pval SIRS pval pval pval SEQ ID  Gene HC SIRS Mild Severe Shock pval HC vs. Severe Shock vs. Shock vs. Number Database ID Name Mean Mean Mean Mean Mean vs. Other ipSIRS vs. Mild Mild Severe 2 NM_020406 CD177 8.091 10.809 11.267 12.088 12.044 0.000000 0.087061 0.048910 0.027926 0.991139 321 NM_020406 CD177 8.091 10.809 11.267 12.088 12.044 0.000000 0.087061 0.048910 0.027926 0.991139 10 NM_001244438 ARG1 5.410 9.054 7.895 8.254 8.919 0.000000 1.000000 0.628534 0.008931 0.209877 329 NM_001244438 ARG1 5.410 9.054 7.895 8.254 8.919 0.000000 1.000000 0.628534 0.008931 0.209877 3 NM_004666 VNN1 7.736 10.013 10.007 10.629 10.876 0.000000 1.000000 0.087388 0.002402 0.671136 322 NM_004666 VNN1 7.736 10.013 10.007 10.629 10.876 0.000000 1.000000 0.087388 0.002402 0.671136 7 NM_080387 CLEC4D 7.187 9.915 9.238 9.152 9.828 0.000000 0.383427 0.945300 0.034026 0.035853 326 NM_080387 CLEC4D 7.187 9.915 9.238 9.152 9.828 0.000000 0.383427 0.945300 0.034026 0.035853 29 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 347 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 24 NM_003855 IL18R1 5.516 8.101 7.098 7.538 8.097 0.000000 1.000000 0.373616 0.001873 0.205385 342 NM_003855 IL18R1 5.516 8.101 7.098 7.538 8.097 0.000000 1.000000 0.373616 0.001873 0.205385 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.000000 0.000068 0.003150 0.006691 0.784808 382 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.000000 0.000068 0.003150 0.006691 0.784808 11 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 6 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.690976 0.351809 325 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.690976 0.351809 14 NM_006433 GNLY 10.653 9.428 9.305 8.659 8.408 0.000000 0.020098 0.014566 0.000045 0.511511 332 NM_006433 GNLY 10.653 9.428 9.305 8.659 8.408 0.000000 0.020098 0.014566 0.000045 0.511511

TABLE 12 Healthy versus Sick Area Under Curve (AUC) SEQ HC vs. Sick HC vs. HC vs. SIRS vs. Mild vs. Mild vs. Severe vs. ID Number Database ID Gene Name AUC inSIRS AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 2 NM_020406 CD177 0.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.675102041 0.540136054 321 NM_020406 CD177 0.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.675102041 0.540136054 7 NM_080387 CLEC4D 0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.671020408 0.691156463 326 NM_080387 CLEC4D 0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.671020408 0.691156463 18 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956 0.639455782 0.749387755 0.644897959 336 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956 0.639455782 0.749387755 0.644897959 3 NM_004666 VNN1 0.979241338 0.996632997 0.972934473 0.663003663 0.648979592 0.710204082 0.575510204 322 NM_004666 VNN1 0.979241338 0.996632997 0.972934473 0.663003663 0.648979592 0.710204082 0.575510204 29 NM_020370 GPR84 0.974313023 0.92704826 0.991452991 0.738927739 0.496598639 0.608163265 0.623129252 347 NM_020370 GPR84 0.974313023 0.92704826 0.991452991 0.738927739 0.496598639 0.608163265 0.623129252 10 NM_001244438 ARG1 0.970878136 0.999438833 0.960520961 0.644355644 0.561904762 0.683265306 0.662585034 329 NM_001244438 ARG1 0.970878136 0.999438833 0.960520961 0.644355644 0.561904762 0.683265306 0.662585034 24 NM_003855 IL18R1 0.966845878 0.989337823 0.958689459 0.62970363 0.62585034 0.715102041 0.639455782 342 NM_003855 IL18R1 0.966845878 0.989337823 0.958689459 0.62970363 0.62585034 0.715102041 0.639455782 26 NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454 0.694971695 0.561904762 0.639183673 0.594557823 344 NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454 0.694971695 0.561904762 0.639183673 0.594557823 5 NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 324 NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 1 NM_003268 TLR5 0.962365591 0.873737374 0.994505495 0.808524809 0.606802721 0.768979592 0.672108844 320 NM_003268 TLR5 0.962365591 0.873737374 0.994505495 0.808524809 0.606802721 0.768979592 0.672108844

TABLE 13 Healthy versus inSIRS Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs. inSIRS HC vs. ipSIRS SIRS vs. Mild vs. Mild vs. Severe vs. Number Database ID Gene Name AUC AUC AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 18 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956 0.639455782 0.749387755 0.644897959 336 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956 0.639455782 0.749387755 0.644897959 10 NM_001244438 ARG1 0.970878136 0.999438833 0.960520961 0.644355644 0.561904762 0.683265306 0.662585034 329 NM_001244438 ARG1 0.970878136 0.999438833 0.960520961 0.644355644 0.561904762 0.683265306 0.662585034 7 NM_080387 CLEC4D 0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.671020408 0.691156463 326 NM_080387 CLEC4D 0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.671020408 0.691156463 5 NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 324 NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 3 NM_004666 VNN1 0.979241338 0.996632997 0.972934473 0.663003663 0.648979592 0.710204082 0.575510204 322 NM_004666 VNN1 0.979241338 0.996632997 0.972934473 0.663003663 0.648979592 0.710204082 0.575510204 26 NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454 0.694971695 0.561904762 0.639183673 0.594557823 344 NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454 0.694971695 0.561904762 0.639183673 0.594557823 17 NM_207113 SLC37A3 0.954301075 0.99382716 0.93996744 0.582417582 0.619047619 0.653061224 0.551020408 335 NM_207113 SLC37A3 0.954301075 0.99382716 0.93996744 0.582417582 0.619047619 0.653061224 0.551020408 38 NM_004994 MMP9 0.935782557 0.993265993 0.914936915 0.625374625 0.653061224 0.653877551 0.48707483 356 NM_004994 MMP9 0.935782557 0.993265993 0.914936915 0.625374625 0.653061224 0.653877551 0.48707483 120 NM_004244 CD163 0.842293907 0.993265993 0.787545788 0.716949717 0.481632653 0.663673469 0.648979592 433 NM_004244 CD163 0.842293907 0.993265993 0.787545788 0.716949717 0.481632653 0.663673469 0.648979592 46 NM_006212 PFKFB2 0.922341697 0.992704826 0.896825397 0.678654679 0.51292517 0.679183673 0.68707483 363 NM_006212 PFKFB2 0.922341697 0.992704826 0.896825397 0.678654679 0.51292517 0.679183673 0.68707483

TABLE 14 Healthy versus ipSIRS Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs. HC vs. SIRS vs. Mild vs. Mild vs. Severe vs. Number Database ID Gene Name AUC inSIRS AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC 1 NM_003268 TLR5 0.962365591 0.873737374 0.994505495 0.808524809 0.606802721 0.768979592 0.672108844 320 NM_003268 TLR5 0.962365591 0.873737374 0.994505495 0.808524809 0.606802721 0.768979592 0.672108844 2 NM_020406 CD177 0.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.675102041 0.540136054 321 NM_020406 CD177 0.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.675102041 0.540136054 29 NM_020370 GPR84 0.974313023 0.92704826 0.991452991 0.738927739 0.496598639 0.608163265 0.623129252 347 NM_020370 GPR84 0.974313023 0.92704826 0.991452991 0.738927739 0.496598639 0.608163265 0.623129252 20 NM_153046 TDRD9 0.929062127 0.758136925 0.991045991 0.844488844 0.640816327 0.734693878 0.636734694 338 NM_153046 TDRD9 0.929062127 0.758136925 0.991045991 0.844488844 0.640816327 0.734693878 0.636734694 4 NM_016021 UBE2J1 0.959677419 0.888327722 0.985551486 0.826173826 0.468027211 0.644897959 0.682993197 323 NM_016021 UBE2J1 0.959677419 0.888327722 0.985551486 0.826173826 0.468027211 0.644897959 0.682993197 11 NR_045213 FCGR1A 0.954749104 0.87037037 0.985347985 0.77988678 0.612244898 0.492244898 0.621768707 NR_045213 FCGR1A 0.954749104 0.87037037 0.985347985 0.77988678 0.612244898 0.492244898 0.621768707 6 NM_002934 RNASE2 0.94937276 0.854657688 0.983719984 0.780552781 0.67755102 0.564081633 0.643537415 325 NM_002934 RNASE2 0.94937276 0.854657688 0.983719984 0.780552781 0.67755102 0.564081633 0.643537415 8 NM_004054 C3AR1 0.950119474 0.868125701 0.97985348 0.832833833 0.529251701 0.609795918 0.582312925 327 NM_004054 C3AR1 0.950119474 0.868125701 0.97985348 0.832833833 0.529251701 0.609795918 0.582312925 12 NM_145018 C11orf82 0.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.650612245 0.555102041 330 NM_145018 C11orf82 0.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.650612245 0.555102041 13 NM_018099 FAR2 0.942502987 0.843434343 0.978428978 0.794205794 0.561904762 0.746122449 0.693877551 331 NM_018099 FAR2 0.942502987 0.843434343 0.978428978 0.794205794 0.561904762 0.746122449 0.693877551

Example 5 Differential Expression of IRS Biomarkersmarkers Between Healthy, inSIRS, Mild Sepsis, Severe Sepsis and Septic Shock

Presented below in FIGS. 13 to 331 are “Box and Whisker” plots for each of the 319 biomarkers where the bottom and top of the box are the first and third quartiles, and the band inside the box is the second quartile (the median) (of gene expression). Biomarkers are presented in order of ascending adjusted p value when comparing “All Classes” (i.e., healthy control, referred to as “Healthy” in FIGS. 13-331; inSIRS, referred to as “SIRS” in FIGS. 13-331; mild sepsis referred to as “Mild” in FIGS. 13-331; severe sepsis, referred to as “Severe” in FIGS. 13-331; and septic shock, referred to as “Shock” in FIGS. 13-331)—varying from 6.49E-48 to 1.00, and according to the following table (Table 15). Appropriate choice and use of such markers can be used to select patients for inclusion in, or exclusion from, clinical trials. Further, such markers can be used to determine the efficacy of treatment, therapies or management regimens in patients by determining whether a patient has transitioned from one condition to another and by determining the stage or degree of a particular condition. For example, an exemplary clinical trial design testing for the efficacy of an inotrope may include only those patients with shock ipSIRS that are most likely to best respond to such a drug. In addition, and following inclusion of such patients and treatment with the inotrope, such patients could be monitored to determine if, when, how quickly and to what degree they respond to the inotrope by their transition from shock ipSIRS to other degrees of ipSIRS, inSIRS or health. Similarly, a model clinical trial design testing for the efficacy of an antibiotic, or combination of antibiotics, may include only those patients with ipSIRS, and not inSIRS, that are most likely to best respond to such a drug. In addition, and following inclusion of such patients and treatment with the antibiotic(s), such patients could be monitored to determine if, when, how quickly and to what degree they respond to the antibiotic(s) by their transition from ipSIRS to inSIRS or health. Similarly, an exemplary clinical trial design testing for the efficacy of an immune modulating drug (e.g. a steroid) may include only those patients with known stages of ipSIRS, for example those recovering from ipSIRS or those in the early stages of ipSIRS. Following inclusion of such patients and treatment with the immune modulating drug, such patients could be monitored to determine if, when, how quickly and to what degree they respond to the immune modulating drug by their transition from ipSIRS to inSIRS or health. The biomarker response and outcome (e.g. reduced length of hospital stay, reduced mortality) of patients in various stages of ipSIRS (early, late) treated with an immune modulating drug may also indicate when such a drug is best administered for maximum benefit.

TABLE 15 Healthy versus inSIRS versus ipSIRS versus Mild versus Severe versus Shock p Value and Area Under Curve (AUC) SEQ ID HC inSIRS Mild Severe Number Database ID Gene Name Mean Mean Mean Mean 1 NM_003268 TLR5 7.747 9.010 9.726 9.979 2 NM_020406 CD177 8.091 10.809 11.267 12.088 3 NM_004666 VNN1 7.736 10.013 10.007 10.629 4 NM_016021 UBE2J1 8.792 9.555 10.118 10.044 5 NM_018285 IMP3 7.951 6.465 7.032 6.934 6 NM_002934 RNASE2 9.164 10.500 11.243 11.670 7 NM_080387 CLEC4D 7.187 9.915 9.238 9.152 8 NM_004054 C3AR1 8.429 9.449 10.261 10.439 9 NM_001145772 GPR56 9.741 8.456 8.297 7.926 10 NM_001244438 ARG1 5.410 9.054 7.895 8.254 11 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 12 NM_145018 C11orf82 5.776 5.888 6.889 7.153 13 NM_018099 FAR2 8.164 8.881 9.322 9.439 14 NM_006433 GNLY 10.653 9.428 9.305 8.659 15 NM_004482 GALNT3 5.685 6.251 6.916 6.728 16 NM_002544 OMG 4.756 5.187 5.644 5.799 17 NM_207113 SLC37A3 8.600 10.048 9.633 9.916 18 NM_203281 BMX 4.804 6.547 6.012 6.397 19 NM_004099 STOM 9.914 10.377 10.824 10.894 20 NM_153046 TDRD9 4.986 5.567 6.483 6.937 21 NM_032045 KREMEN1 8.626 9.409 10.143 10.189 22 NM_005449 FAIM3 10.259 9.101 9.036 9.174 23 NM_014358 CLEC4E 8.446 10.547 9.491 9.618 24 NM_003855 IL18R1 5.516 8.101 7.098 7.538 25 NM_018367 ACER3 7.317 7.845 8.701 8.417 26 NM_006459 ERLIN1 7.136 9.162 8.418 8.526 27 NM_004612 TGFBR1 9.328 9.614 10.165 9.999 28 NM_001145775 FKBP5 9.006 11.106 10.185 10.457 29 NM_020370 GPR84 6.712 8.157 9.030 8.980 30 NM_182597 C7orf53 6.397 6.878 7.266 7.760 31 NM_153021 PLB1 8.205 8.887 9.574 9.463 32 NM_013352 DSE 7.272 7.680 8.183 8.109 33 NM_000953 PTGDR 9.310 8.373 8.028 7.790 34 NM_001744 CAMK4 8.155 6.723 6.902 7.152 35 NM_015268 DNAJC13 7.507 8.083 8.596 8.693 36 NM_007115 TNFAIP6 7.738 9.246 9.829 9.631 37 NM_199135 FOXD4L3 6.441 6.501 7.402 7.610 38 NM_004994 MMP9 10.179 12.012 11.383 11.801 39 NM_000637 GSR 8.866 9.322 9.492 10.037 40 NM_016523 KLRF1 6.343 5.438 5.022 4.504 41 NM_053282 SH2D1B 8.067 6.992 6.896 6.451 42 NM_001004441 ANKRD34B 4.809 5.415 5.855 6.471 43 NM_001136258 SGMS2 6.693 7.708 7.553 7.712 44 NM_032047 B3GNT5 6.871 8.033 8.009 7.744 45 NR_026575 GK3P 4.227 5.729 5.316 5.552 46 NM_006212 PFKFB2 7.955 10.444 9.336 9.326 47 NM_007166 PICALM 9.079 9.433 9.822 9.993 48 NM_152637 METTL7B 6.693 7.153 7.628 7.927 49 NM_003542 HIST1H4C 11.803 9.795 10.815 10.617 50 NM_145005 C9orf72 8.262 8.742 9.312 9.073 51 NM_003533 HIST1H3I 10.878 9.003 10.144 10.141 52 NM_021082 SLC15A2 7.246 7.309 7.861 8.034 53 NM_030956 TLR10 6.794 6.842 7.713 7.823 54 NM_001124 ADM 8.676 8.896 9.739 9.441 55 NM_014143 CD274 5.508 5.656 7.557 7.211 56 NM_001311 CRIP1 8.880 6.932 7.984 7.844 57 NM_001099660 LRRN3 7.163 5.997 5.841 6.367 58 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971 59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 60 NM_006714 SMPDL3A 6.243 6.701 7.288 7.563 61 NM_005531 IFI16 8.973 10.323 9.950 9.942 62 NM_016475 JKAMP 8.440 8.442 9.231 8.827 63 ENST00000371443 MRPL41 8.037 6.496 7.288 7.313 64 NM_004172 SLC1A3 5.849 6.892 6.472 6.447 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 66 NM_001164116 CASS4 8.124 7.848 7.410 7.217 67 ENST00000533734 TCN1 6.015 6.936 7.241 8.481 68 NM_018639 WSB2 8.870 8.808 9.618 9.714 69 ENST00000405140 CLU 9.016 9.264 9.889 10.137 70 NM_001163278 ODZ1 6.088 7.677 6.668 7.303 71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 72 NM_001130715 PLAC8 10.873 10.024 11.434 11.500 73 NM_001257400 CD63 9.235 9.126 9.718 9.990 74 NM_006665 HPSE 8.103 8.173 9.127 9.174 75 NM_152367 C1orf161 5.851 6.354 6.479 6.738 76 ENST00000443533 DDAH2 8.067 8.170 8.630 8.707 77 NM_001199805 KLRK1 8.677 7.641 7.492 7.142 78 NM_024524 ATP13A3 7.668 7.763 8.429 8.547 79 NM_005546 ITK 9.271 8.099 8.227 8.536 80 NM_021127 PMAIP1 6.940 5.860 6.770 6.251 81 NR_046099 LOC284757 6.913 8.028 6.894 7.534 82 NM_002080 GOT2 6.854 5.823 6.017 6.378 83 NR_036641 PDGFC 6.098 6.117 6.987 7.044 84 NM_012200 B3GAT3 7.939 7.030 7.516 7.658 85 NM_003545 HIST1H4E 10.534 9.717 9.907 9.362 86 NM_000860 HPGD 5.621 6.238 7.085 6.908 87 NM_031950 FGFBP2 8.090 7.266 7.130 6.722 88 NM_181506 LRRC70 3.455 3.495 4.144 3.763 89 NM_018342 TMEM144 5.697 6.377 6.781 7.043 90 gi|21749325 CDS2 10.235 9.988 10.651 10.561 91 NM_001725 BPI 7.724 8.603 8.894 10.119 92 ENST00000379215 ECHDC3 7.486 8.705 7.801 7.715 93 NM_001837 CCR3 7.078 6.226 6.015 6.079 94 NM_014181 HSPC159 9.120 9.779 9.933 10.248 95 NM_018324 OLAH 4.483 6.220 5.483 6.162 96 NM_006243 PPP2R5A 8.141 9.000 8.646 8.931 97 NM_001193451 TMTC1 6.316 7.153 7.497 7.783 98 NM_001023570 EAF2 8.389 8.607 9.323 9.016 99 NM_001268 RCBTB2 8.646 8.621 9.321 9.357 100 NM_021982 SEC24A 7.847 7.914 8.266 8.571 101 NM_001017995 SH3PXD2B 5.800 6.949 6.262 6.384 102 NM_001130688 HMGB2 7.782 9.225 8.769 8.869 103 ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 104 NM_001276 CHI3L1 10.470 9.876 8.640 9.035 105 NM_174938 FRMD3 6.218 6.408 6.959 6.889 106 NM_018375 SLC39A9 8.038 7.719 8.121 8.368 107 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 108 NM_016476 ANAPC11 6.716 5.940 6.219 6.214 109 NM_019037 EXOSC4 8.216 8.199 8.716 8.845 110 gi|13182974 NA 7.793 8.895 8.712 8.321 111 NM_198336 INSIG1 8.081 7.370 8.062 7.867 112 ENST00000542161 FOLR3 7.767 8.283 8.505 9.059 113 NM_001024630 RUNX2 9.359 9.363 8.874 8.973 114 NM_018457 PRR13 8.919 9.881 9.380 9.204 115 NM_003546 HIST1H4L 9.807 7.908 9.466 9.602 116 NM_002305 LGALS1 10.393 10.059 10.730 10.741 117 NM_001295 CCR1 9.036 9.458 10.071 10.000 118 NM_003596 TPST1 8.526 10.334 9.295 9.600 119 NM_019111 HLA-DRA 11.467 10.758 10.870 10.441 120 NM_004244 CD163 6.823 8.730 7.652 7.643 121 NM_005306 FFAR2 9.559 9.849 10.479 10.309 122 NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 123 NM_005729 PPIF 9.131 8.392 8.994 8.669 124 NM_001199760 MTHFS 8.177 9.060 8.644 8.530 125 NM_015190 DNAJC9 7.382 5.889 6.594 7.251 126 NM_005564 LCN2 7.729 8.168 8.921 10.113 127 ENST00000233057 EIF2AK2 7.237 8.670 8.347 8.159 128 NM_006498 LGALS2 6.920 6.085 6.594 6.235 129 NM_001199922 SIAE 6.721 6.530 7.174 7.284 130 NM_004644 AP3B2 5.979 6.181 6.485 6.665 131 NM_152701 ABCA13 5.814 6.131 6.688 7.350 132 gi|21757933 NA 7.336 7.963 7.065 7.340 133 NR_026586 EFCAB2 4.462 4.942 5.648 4.939 134 NM_170745 HIST1H2AA 6.310 6.872 6.483 6.652 135 NR_024610 HINT1 7.948 6.705 7.591 7.235 136 NM_003535 HIST1H3J 8.323 6.703 7.484 7.314 137 NM_001785 CDA 10.407 11.415 11.045 11.032 138 NM_003864 SAP30 9.022 9.883 9.258 9.251 139 NM_001040196 AGTRAP 10.055 9.933 10.594 10.356 140 NM_033050 SUCNR1 3.660 3.734 4.329 4.517 141 NM_002454 MTRR 8.163 7.862 8.523 8.796 142 NM_001168357 PLA2G7 6.797 6.685 5.928 5.753 143 NM_016108 AIG1 6.298 6.237 6.932 6.735 144 NM_013363 PCOLCE2 5.863 5.909 6.292 6.366 145 NM_080491 GAB2 9.471 10.280 9.787 9.810 146 NM_012262 HS2ST1 7.018 6.874 7.363 7.378 147 NM_003529 HIST1H3A 7.822 6.477 7.240 7.007 148 gi|21757754 C22orf37 8.047 7.525 8.170 7.898 149 ENST00000443117 HLA-DPA1 11.917 11.327 11.426 10.930 150 NM_030796 VOPP1 9.302 8.771 9.510 9.318 151 NM_001135147 SLC39A8 7.820 7.364 8.061 7.951 152 NM_002417 MKI67 5.979 5.822 6.140 6.894 153 NM_000578 SLC11A1 10.812 11.719 11.333 11.110 154 NM_001657 AREG 6.075 6.806 6.139 6.126 155 NM_005502 ABCA1 7.734 7.909 8.601 8.816 156 NM_001201427 DAAM2 6.591 8.217 7.135 7.167 157 NM_002343 LTF 8.098 8.330 8.923 9.937 158 NM_178174 TREML1 8.869 9.169 9.868 10.187 159 NM_004832 GSTO1 7.113 7.036 7.593 7.598 160 NM_000956 PTGER2 8.918 8.348 9.019 8.983 161 NM_001816 CEACAM8 7.336 7.874 8.503 9.775 162 NM_016184 CLEC4A 8.210 8.289 9.043 8.708 163 NR_002217 PMS2CL 7.598 6.664 7.289 7.057 164 NM_001193374 RETN 7.747 7.886 8.097 8.248 165 NM_000922 PDE3B 8.142 8.242 7.627 7.734 166 NM_018837 SULF2 9.831 9.704 9.064 9.248 167 NM_001145001 NEK6 9.503 9.287 9.835 9.710 168 NM_022145 CENPK 7.073 6.142 6.664 6.040 169 NM_145725 TRAF3 8.046 7.482 8.145 8.152 170 NM_003608 GPR65 9.085 9.452 9.834 9.216 171 NR_046000 IRF4 8.200 7.491 7.843 8.381 172 gi|42521648 MACF1 6.473 6.545 7.013 7.187 173 NM_001144 AMFR 9.420 8.994 9.671 9.823 174 NM_000985 RPL17 6.122 5.182 5.758 5.556 175 NM_003749 IRS2 8.965 9.531 8.841 8.998 176 NM_002230 JUP 8.165 7.803 7.812 7.804 177 NM_013230 CD24 5.563 5.793 6.173 7.205 178 NM_004481 GALNT2 8.541 8.534 8.896 8.980 179 NM_007355 HSP90AB1 9.881 8.635 9.085 9.720 180 NM_024656 GLT25D1 9.757 9.336 9.948 9.861 181 NM_001001658 OR9A2 4.207 4.662 4.825 4.991 182 NM_001178135 HDHD1A 8.039 7.927 8.296 8.300 183 NM_001141945 ACTA2 6.977 6.906 7.430 7.434 184 NM_152282 ACPL2 6.821 7.762 7.139 7.253 185 NM_001137550 LRRFIP1 6.512 6.814 6.827 7.037 186 NM_001161352 KCNMA1 5.664 6.042 6.492 6.317 187 gi|12584148 OCR1 8.817 9.952 8.387 8.844 188 NM_000885 ITGA4 8.779 7.932 8.394 7.981 189 NM_001412 EIF1AX 7.439 6.463 7.251 6.667 190 NM_176818 SFRS9 9.739 9.666 9.715 10.226 191 NM_206831 DPH3 6.211 6.602 6.923 6.606 192 NM_001031711 ERGIC1 9.539 10.203 9.742 9.565 193 NM_007261 CD300A 9.890 9.479 10.058 10.054 194 NM_001085386 NF-E4 7.348 8.202 7.715 8.545 195 NM_004897 MINPP1 8.212 7.697 8.228 7.151 196 NM_003141 TRIM21 8.072 8.151 8.840 8.371 197 NM_006969 ZNF28 4.936 4.511 5.008 4.554 198 gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 199 gi|15530286 NA 9.276 9.224 8.804 8.909 200 gi|7021995 NA 7.607 8.002 6.917 7.075 201 NM_000201 ICAM1 8.842 8.625 9.470 9.156 202 NM_005645 TAF13 5.332 5.173 5.949 5.474 203 NM_000917 P4HA1 6.365 6.096 6.773 6.664 204 NM_207445 C15orf54 4.953 4.588 4.394 4.290 205 NM_002108 HAL 7.142 6.909 7.654 7.331 206 NM_015998 KLHL5 9.003 9.971 9.113 9.407 207 NR_002612 DLEU2 6.549 6.894 7.347 6.699 208 NM_015199 ANKRD28 7.286 7.393 7.898 7.722 209 ENST00000375864 LY6G5B 9.037 8.992 8.780 8.654 210 ENST00000344062 KIAA1257 6.868 7.365 6.882 6.874 211 NM_004528 MGST3 9.104 8.519 9.273 9.098 212 NM_015187 KIAA0746 8.174 7.591 8.170 8.747 213 NM_001540 HSPB1 9.140 8.923 9.664 9.580 214 NM_005508 CCR4 7.105 6.356 6.598 6.829 215 NM_001071 TYMS 6.084 5.695 6.186 6.854 216 ENST00000536831 RRP12 8.946 8.381 8.821 8.752 217 NM_176816 CCDC125 7.600 8.401 7.883 8.048 218 NM_003521 HIST1H2BM 10.104 9.242 10.213 10.837 219 NM_002612 PDK4 7.445 8.411 8.080 8.035 220 NM_207627 ABCG1 8.318 7.923 7.960 8.214 221 NM_000576 IL1B 9.070 9.172 10.021 9.550 222 NM_003246 THBS1 8.599 9.860 8.993 9.423 223 NM_000419 ITGA2B 8.899 8.768 9.482 9.747 224 NM_005780 LHFP 6.216 6.391 6.523 6.665 225 NM_002287 LAIR1 9.265 9.118 9.815 9.654 226 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501 227 gi|29387167 ZRANB1 8.205 9.041 8.641 8.455 228 ENST00000525158 TIMM10 7.454 6.704 7.473 7.175 229 NM_207647 FSD1L 4.605 4.402 4.801 4.565 230 NM_021066 HIST1H2AJ 5.699 4.152 4.990 3.871 231 ENST00000362012 PTGS1 8.883 8.605 9.293 9.429 232 gi|14250459 NA 6.389 5.970 6.875 5.976 233 NM_080678 UBE2F 7.698 7.618 8.235 8.225 234 NM_001104595 FAM118A 8.366 7.706 7.946 8.222 235 NM_003531 HIST1H3C 9.254 7.630 9.025 9.427 236 NM_003965 CCRL2 6.401 6.488 6.982 6.754 237 NR_003094 E2F6 4.235 3.673 4.143 3.579 238 NM_198275 MPZL3 10.241 10.754 10.209 10.039 239 NM_080725 SRXN1 9.497 9.560 9.732 9.815 240 NM_004357 CD151 9.083 8.712 9.309 9.522 241 NM_003536 HIST1H3H 9.933 8.623 9.688 9.538 242 NM_031919 FSD1L 2.752 2.614 2.977 2.804 243 NM_001131065 RFESD 6.745 6.242 6.502 5.562 244 NM_012112 TPX2 5.722 5.535 5.917 6.431 245 NM_006272 S100B 5.289 4.661 4.917 4.533 246 NM_032828 ZNF587 8.514 8.816 8.783 8.381 247 NM_152501 PYHIN1 8.390 8.040 7.744 7.890 248 NM_020775 KIAA1324 9.383 9.433 8.708 9.010 249 NM_002483 CEACAM6 5.249 5.373 5.959 6.957 250 NM_001130415 APOLD1 7.318 6.858 7.205 7.363 251 NM_000134 FABP2 4.244 4.518 4.396 4.549 252 NM_001080424 KDM6B 9.854 10.383 10.147 9.749 253 ENST00000390265 IGK@ 10.393 9.201 10.307 11.107 254 NM_006097 MYL9 9.235 9.445 10.021 9.931 255 NM_021058 HIST1H2BJ 5.949 5.710 6.330 6.446 256 NM_138327 TAAR1 5.009 5.081 5.282 5.490 257 NM_001828 CLC 10.866 9.692 9.618 9.860 258 NM_001199208 CYP4F3 9.187 10.093 9.402 9.895 259 NM_024548 CEP97 6.566 6.717 6.887 7.228 260 NM_138927 SON 8.449 7.921 8.330 7.930 261 NM_002198 IRF1 10.490 10.067 10.764 10.511 262 NM_182914 SYNE2 7.764 8.208 7.453 7.752 263 NM_000902 MME 9.625 10.744 9.401 10.054 264 NM_024552 LASS4 8.222 7.808 7.961 8.050 265 NM_001925 DEFA4 7.421 7.383 8.289 9.454 266 NM_024913 C7orf58 7.727 6.800 7.706 7.617 267 ENST00000549649 DYNLL1 7.250 7.328 8.200 7.946 268 gi|38532374 NA 5.269 5.274 4.828 4.979 269 NM_000250 MPO 7.605 7.565 8.053 8.694 270 NM_001874 CPM 5.874 6.569 5.926 5.965 271 NM_173485 TSHZ2 7.382 6.972 6.846 7.429 272 NR_038064 PLIN2 8.210 8.534 8.470 8.589 273 NM_024556 FAM118B 7.287 7.256 7.846 7.892 274 NM_001199873 B4GALT3 9.539 8.790 9.278 9.265 275 NM_006989 RASA4 8.298 8.139 7.796 8.031 276 NM_001257971 CTSL1 6.074 5.925 6.577 6.106 277 NM_000270 NP 9.487 9.103 9.780 10.001 278 NM_001130059 ATF7 5.212 5.212 5.253 5.605 279 NM_003118 SPARC 9.083 9.207 9.737 9.964 280 NM_153021 PLB1 6.867 7.077 7.318 7.615 281 NM_001170330 C4orf3 7.478 7.240 7.324 7.402 282 NM_002692 POLE2 7.205 6.613 7.184 6.047 283 NM_001192 TNFRSF17 4.474 4.005 4.587 5.008 284 NM_145032 FBXL13 6.474 6.987 6.791 7.387 285 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 286 NM_024956 TMEM62 7.599 7.189 7.875 7.664 287 NM_052960 RBP7 7.270 7.808 7.218 7.267 288 NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 289 NM_002923 RGS2 11.584 12.239 11.737 11.649 290 NM_004691 ATP6V0D1 11.562 11.584 11.951 11.656 291 NM_144563 RPIA 9.444 9.221 8.913 8.388 292 NM_020397 CAMK1D 9.001 9.118 8.603 8.572 293 NM_016232 IL1RL1 5.573 6.273 5.757 6.219 294 NM_138460 CMTM5 7.473 7.266 7.853 7.851 295 NM_004847 AIF1 8.167 8.310 8.735 8.045 296 NM_001928 CFD 10.389 9.779 9.631 9.496 297 NM_144765 MPZL2 7.015 7.320 6.845 6.660 298 gi|27884043 LOC100128751 8.552 8.877 8.333 8.552 299 NM_144646 IGJ 8.646 7.900 8.962 10.005 300 NM_139286 CDC26 7.968 7.634 8.113 7.886 301 NM_006241 PPP1R2 7.446 7.738 7.546 7.286 302 NM_000564 IL5RA 6.871 6.298 6.092 6.455 303 NM_001113738 ARL17P1 8.846 8.829 8.802 8.293 304 NR_033759 ATP5L 7.242 7.337 7.379 6.824 305 NM_176885 TAS2R31 6.228 5.589 5.847 6.020 306 NM_001024599 HIST2H2BF 8.854 9.615 9.386 9.211 307 NM_001743 CALM2 8.475 9.041 9.003 8.357 308 NM_019073 SPATA6 6.797 7.301 7.095 6.806 309 ENST00000390285 IGLV6-57 5.779 5.379 6.029 6.575 310 NM_020362 C1orf128 9.258 9.026 8.605 8.315 311 NM_181623 KRTAP15-1 6.250 6.690 6.548 6.617 312 NM_006417 IFI44 6.559 6.924 8.107 6.674 313 NM_001178126 IGL@ 7.052 6.606 7.027 7.343 314 gi|21707823 NA 4.903 5.476 4.955 4.639 315 NM_003001 SDHC 7.530 6.874 7.879 7.593 316 NM_152995 NFXL1 7.326 6.876 7.694 7.644 317 NM_000170 GLDC 5.599 5.395 5.679 5.770 318 NM_001199743 DCTN5 8.646 8.336 8.737 8.794 319 NM_014736 KIAA0101 4.443 3.951 4.537 4.749 pval pval SEQ ID Shock pval HC inSIRS vs pval pval Shock vs Number Mean vs Other Sepsis Severe vs Mild Shock vs Mild Severe 1 10.311 0.000000 0.000225 0.275728 0.000244 0.110996 2 12.044 0.000000 0.087061 0.048910 0.027926 0.991139 3 10.876 0.000000 1.000000 0.087388 0.002402 0.671136 4 10.335 0.000000 0.000015 0.817659 0.104460 0.049488 5 6.723 0.000000 0.000000 0.645365 0.004479 0.136619 6 11.388 0.000000 0.002979 0.095954 0.690976 0.351809 7 9.828 0.000000 0.383427 0.945300 0.034026 0.035853 8 10.593 0.000000 0.000016 0.650271 0.142678 0.725241 9 7.611 0.000000 0.009068 0.118387 0.000147 0.212773 10 8.919 0.000000 1.000000 0.628534 0.008931 0.209877 11 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 12 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 13 9.813 0.000000 0.000434 0.708180 0.000658 0.034215 14 8.408 0.000000 0.020098 0.014566 0.000045 0.511511 15 7.075 0.000000 0.000000 0.407446 0.422801 0.051201 16 6.063 0.000000 0.000000 0.486295 0.001484 0.125031 17 9.990 0.000000 1.000000 0.200617 0.035680 0.892137 18 6.839 0.000000 1.000000 0.251812 0.000431 0.163472 19 11.148 0.000000 0.000000 0.861884 0.017391 0.144729 20 7.385 0.000000 0.000000 0.248195 0.001153 0.259068 21 10.055 0.000000 0.000079 0.962640 0.837239 0.731337 22 8.464 0.000000 1.000000 0.732360 0.001453 0.000582 23 9.945 0.000000 0.000082 0.796454 0.024690 0.225126 24 8.097 0.000000 1.000000 0.373616 0.001873 0.205385 25 9.050 0.000000 0.000000 0.362961 0.132905 0.008450 26 8.855 0.000000 0.431436 0.880592 0.070482 0.316561 27 10.368 0.000000 0.000000 0.333550 0.115281 0.005971 28 10.750 0.000000 0.035769 0.441852 0.011707 0.389198 29 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 30 7.532 0.000000 0.000080 0.007994 0.141534 0.338948 31 10.019 0.000000 0.000133 0.872838 0.059398 0.037699 32 8.384 0.000000 0.000062 0.830987 0.170622 0.085371 33 7.577 0.000000 0.007043 0.500548 0.040553 0.570947 34 6.470 0.000000 1.000000 0.305982 0.011176 0.000339 35 8.878 0.000000 0.000000 0.833718 0.133216 0.512817 36 9.738 0.000000 1.000000 0.712067 0.908467 0.905260 37 7.649 0.000000 0.000000 0.417434 0.196375 0.968937 38 11.830 0.000000 1.000000 0.196819 0.086637 0.992129 39 9.928 0.000000 0.000039 0.000432 0.001207 0.709854 40 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 41 6.450 0.000000 0.652812 0.064249 0.026676 0.999993 42 6.738 0.000000 0.000000 0.074815 0.001252 0.606835 43 8.128 0.000000 1.000000 0.670176 0.001769 0.073073 44 8.548 0.000000 1.000000 0.438680 0.013149 0.000955 45 5.937 0.000000 1.000000 0.493837 0.002433 0.158402 46 10.196 0.000000 0.069041 0.999591 0.014557 0.037351 47 10.284 0.000000 0.000000 0.500534 0.001958 0.138243 48 8.037 0.000000 0.000000 0.315445 0.060594 0.854279 49 10.323 0.000000 0.000004 0.695152 0.055254 0.448113 50 9.243 0.000000 0.000039 0.180477 0.821921 0.417016 51 9.669 0.000000 0.000000 0.999875 0.029147 0.070614 52 8.227 0.000000 0.000000 0.350572 0.002949 0.273300 53 7.900 0.000000 0.000000 0.798408 0.423055 0.895083 54 9.674 0.000000 0.000001 0.091052 0.855508 0.225820 55 7.237 0.000000 0.000000 0.536662 0.490374 0.996684 56 7.526 0.000000 0.002476 0.799183 0.046887 0.320589 57 5.906 0.000000 1.000000 0.002710 0.877917 0.009767 58 9.578 0.000000 0.067333 0.026428 0.000013 0.253098 59 8.353 0.000000 0.297018 0.000084 0.000000 0.708038 60 8.227 0.000000 0.000000 0.609142 0.000879 0.060210 61 9.860 0.000000 0.020087 0.998663 0.799026 0.869508 62 9.150 0.000000 0.000000 0.000984 0.661905 0.010409 63 7.279 0.000000 0.000000 0.986016 0.997776 0.974877 64 7.373 0.000000 1.000000 0.993673 0.000056 0.000352 65 9.322 0.000000 0.000068 0.003150 0.006691 0.784808 66 7.047 0.000000 0.000000 0.428924 0.022397 0.521806 67 8.420 0.000000 0.000290 0.005182 0.001932 0.986095 68 9.657 0.000000 0.000000 0.761189 0.939475 0.910197 69 9.813 0.000000 0.000063 0.216377 0.819582 0.075588 70 7.416 0.000000 0.245652 0.024855 0.001421 0.884318 71 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 72 11.772 1.000000 0.000000 0.950268 0.172935 0.420732 73 10.159 0.000000 0.000000 0.156468 0.002260 0.485665 74 8.937 0.000000 0.000000 0.958822 0.401504 0.346256 75 6.845 0.000000 0.417891 0.196267 0.015651 0.756446 76 9.015 0.000000 0.000000 0.868573 0.011535 0.108528 77 6.815 0.000000 1.000000 0.344640 0.006675 0.394595 78 8.804 0.000000 0.000000 0.777976 0.039725 0.310273 79 7.635 0.000000 1.000000 0.276607 0.002832 0.000063 80 6.214 0.000000 0.000000 0.002936 0.000187 0.968287 81 7.376 0.000001 0.000004 0.000039 0.000364 0.493398 82 6.047 0.000000 0.490985 0.040847 0.969911 0.066504 83 7.466 0.000000 0.000000 0.970637 0.064634 0.196970 84 7.632 0.000000 0.000000 0.387020 0.434149 0.966275 85 9.307 0.000000 1.000000 0.026325 0.003375 0.962707 86 7.946 0.000000 0.000024 0.895298 0.035905 0.027021 87 6.520 0.000000 0.976589 0.222869 0.013639 0.687542 88 4.135 0.000000 0.000000 0.020091 0.996732 0.024012 89 6.824 0.000000 0.914921 0.404820 0.968067 0.529328 90 10.570 0.041844 0.000000 0.584395 0.561379 0.994393 91 9.589 0.000000 0.026705 0.001145 0.046592 0.253799 92 8.165 0.000000 0.001975 0.857762 0.030361 0.018763 93 5.783 0.000000 1.000000 0.937302 0.322103 0.253166 94 10.249 0.000000 1.000000 0.218914 0.132537 0.999989 95 6.492 0.000000 1.000000 0.125688 0.003063 0.604976 96 8.952 0.000000 1.000000 0.109345 0.035683 0.988342 97 7.961 0.000000 0.543009 0.580949 0.154696 0.809916 98 9.301 0.000000 0.000012 0.105531 0.985106 0.141523 99 9.340 0.000000 0.000001 0.961397 0.985058 0.991536 100 8.641 0.000000 0.000000 0.094884 0.010201 0.880791 101 6.870 0.000000 1.000000 0.786615 0.000756 0.026622 102 8.910 0.000000 1.000000 0.894388 0.745151 0.981575 103 7.123 0.000000 0.011838 0.056985 0.008125 0.944270 104 8.726 0.000000 0.000056 0.485576 0.954853 0.641602 105 6.703 0.000000 0.000000 0.872556 0.096505 0.389383 106 8.428 1.000000 0.000000 0.034062 0.001276 0.808136 107 8.112 0.000000 1.000000 0.310769 0.000008 0.014285 108 6.368 0.000000 0.016248 0.998808 0.245788 0.323554 109 9.307 0.000000 0.000000 0.796042 0.002754 0.059157 110 8.114 0.000000 0.003518 0.039103 0.000094 0.391472 111 7.913 0.001237 0.000000 0.123875 0.197540 0.883915 112 9.163 0.000000 0.039807 0.074391 0.008772 0.909407 113 8.703 0.000000 0.000000 0.683914 0.223731 0.064525 114 9.375 0.000000 0.000085 0.390820 0.998964 0.411767 115 9.065 0.000032 0.000000 0.878290 0.231084 0.140998 116 10.938 0.246894 0.000000 0.995433 0.137821 0.261564 117 10.253 0.000000 0.009681 0.937695 0.574455 0.448234 118 9.760 0.000000 0.055822 0.518907 0.136190 0.833502 119 10.188 0.000000 1.000000 0.091925 0.000575 0.429491 120 8.413 0.000000 0.092636 0.999632 0.037288 0.077812 121 10.573 0.000000 0.002329 0.564897 0.791331 0.256398 122 10.185 0.000000 0.048380 0.088690 0.661703 0.354179 123 8.682 0.000000 0.001262 0.016029 0.006615 0.993122 124 8.897 0.000000 0.406481 0.678016 0.086391 0.022355 125 6.665 0.000000 0.000027 0.007512 0.921502 0.019348 126 10.092 0.000000 0.000002 0.028722 0.011016 0.998889 127 8.071 0.000000 0.263528 0.709245 0.379350 0.929139 128 6.142 0.000000 0.375827 0.045528 0.001960 0.806041 129 7.068 0.000300 0.000000 0.666202 0.603123 0.213378 130 7.116 0.000000 0.000003 0.646507 0.001477 0.071475 131 7.182 0.000000 0.000000 0.066387 0.130673 0.834277 132 7.275 1.000000 0.000000 0.034322 0.071492 0.818353 133 5.021 0.000000 1.000000 0.000756 0.000575 0.898330 134 6.820 0.000002 0.020011 0.118625 0.000044 0.124349 135 7.052 0.000000 0.041290 0.142709 0.003549 0.590766 136 7.135 0.000000 0.000274 0.715862 0.160141 0.691744 137 11.020 0.000000 0.043371 0.996584 0.982475 0.996794 138 9.295 0.000000 0.001328 0.998061 0.924872 0.921435 139 10.502 0.000787 0.000000 0.083006 0.602023 0.382046 140 4.334 0.000000 0.000000 0.487465 0.999396 0.504866 141 8.849 0.122045 0.000000 0.216837 0.057755 0.942570 142 5.816 0.000000 0.011194 0.534519 0.708535 0.921793 143 7.005 0.000001 0.000000 0.470358 0.871019 0.246707 144 6.785 0.000000 0.000000 0.925694 0.013235 0.089534 145 9.689 0.000000 0.000000 0.982341 0.635805 0.599323 146 7.465 0.001571 0.000000 0.990909 0.556564 0.723490 147 6.871 0.000000 0.000513 0.477800 0.090217 0.776827 148 7.869 0.152851 0.000000 0.021435 0.002302 0.954990 149 10.582 0.000000 1.000000 0.125876 0.000597 0.352649 150 9.517 1.000000 0.000000 0.298375 0.997162 0.269787 151 8.434 1.000000 0.000000 0.788039 0.031226 0.013434 152 6.463 0.105108 0.000000 0.000163 0.097385 0.045251 153 11.214 0.000000 0.000048 0.338648 0.659426 0.788341 154 6.310 0.001304 0.023771 0.980680 0.018616 0.030209 155 8.449 0.000000 0.000000 0.574359 0.692624 0.204976 156 7.607 0.000000 0.072069 0.992564 0.124949 0.254325 157 9.639 0.000000 0.000019 0.012909 0.052398 0.670808 158 9.876 0.000000 0.000638 0.449927 0.999311 0.468015 159 7.768 0.000017 0.000000 0.999247 0.345252 0.470840 160 9.315 1.000000 0.000000 0.970366 0.070654 0.081972 161 9.287 0.000000 0.001298 0.011921 0.098854 0.501991 162 9.051 0.000001 0.000030 0.153416 0.998827 0.141809 163 7.040 0.000000 0.000440 0.281314 0.146553 0.993528 164 8.835 0.000000 0.000109 0.767125 0.000526 0.022922 165 7.730 0.000586 0.000010 0.567468 0.494918 0.999337 166 8.696 0.000000 0.000000 0.721156 0.183689 0.059640 167 9.943 0.153083 0.000000 0.541334 0.546065 0.125355 168 6.012 0.000000 1.000000 0.003871 0.000401 0.988210 169 7.882 1.000000 0.000003 0.997488 0.011707 0.028412 170 9.665 0.000000 1.000000 0.000115 0.360571 0.006354 171 7.762 0.000105 0.000070 0.002770 0.825556 0.000501 172 7.377 0.000009 0.000481 0.609788 0.061186 0.553735 173 9.635 1.000000 0.000000 0.543847 0.954865 0.395265 174 5.513 0.000000 0.000708 0.298432 0.096378 0.944801 175 9.024 1.000000 0.000068 0.322209 0.133169 0.969809 176 7.467 0.000000 1.000000 0.997835 0.006010 0.023849 177 7.139 0.000000 0.044339 0.008714 0.004161 0.979397 178 9.333 0.000001 0.000000 0.887956 0.017521 0.130219 179 9.186 0.000000 0.052850 0.015572 0.861439 0.050094 180 10.201 1.000000 0.000001 0.826970 0.124352 0.061226 181 5.108 0.000000 1.000000 0.671976 0.216358 0.816042 182 8.508 0.156481 0.000026 0.999441 0.101404 0.187976 183 7.417 0.000137 0.000000 0.999465 0.993242 0.991119 184 7.209 0.000000 0.038264 0.736975 0.857652 0.955277 185 7.241 0.000015 1.000000 0.216163 0.000656 0.232230 186 7.025 0.000000 0.031350 0.804476 0.075606 0.033982 187 8.270 1.000000 0.000000 0.273663 0.890199 0.131897 188 7.906 0.000000 1.000000 0.064191 0.007047 0.909442 189 6.907 0.000022 0.033776 0.002586 0.056446 0.338019 190 10.128 1.000000 0.000618 0.000018 0.000064 0.627141 191 6.842 0.000000 1.000000 0.052319 0.764333 0.189405 192 9.579 0.014810 0.000006 0.364984 0.320097 0.993728 193 10.025 1.000000 0.000000 0.999363 0.929522 0.957280 194 7.934 0.000000 1.000000 0.002063 0.535657 0.030775 195 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 196 8.271 0.002474 0.019577 0.006536 0.000092 0.782377 197 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 198 4.817 0.000001 1.000000 0.000589 0.919120 0.001847 199 8.390 0.001392 0.000629 0.821276 0.021614 0.011283 200 6.999 0.480277 0.000000 0.721694 0.889525 0.926763 201 9.088 0.069361 0.000002 0.113961 0.015602 0.900519 202 5.798 0.054434 0.000001 0.004467 0.457202 0.072895 203 6.873 1.000000 0.000000 0.752172 0.731181 0.358637 204 4.370 0.000000 0.891418 0.669439 0.972296 0.787051 205 7.571 0.894890 0.000000 0.049449 0.758572 0.184215 206 9.083 0.009758 0.000000 0.315312 0.984513 0.248776 207 6.850 0.000171 1.000000 0.000019 0.000152 0.504529 208 7.941 0.000001 0.046283 0.544926 0.951892 0.391621 209 8.383 0.014059 0.001656 0.642377 0.004351 0.135693 210 6.689 1.000000 0.000000 0.997049 0.129430 0.242441 211 9.003 1.000000 0.000001 0.377410 0.050613 0.751799 212 8.502 1.000000 0.000002 0.015387 0.148674 0.453449 213 9.371 0.267148 0.000000 0.845749 0.072845 0.359260 214 6.321 0.000000 1.000000 0.321399 0.120202 0.005850 215 6.291 1.000000 0.000000 0.003309 0.816061 0.015734 216 8.721 0.000127 0.000063 0.794423 0.522969 0.952728 217 7.988 0.000029 0.000070 0.517640 0.700053 0.915690 218 10.261 1.000000 0.000000 0.055178 0.976895 0.083087 219 8.318 0.000000 1.000000 0.982556 0.521742 0.501879 220 7.791 0.000000 1.000000 0.112606 0.270961 0.003184 221 9.930 0.000002 0.124840 0.077098 0.874756 0.185371 222 9.387 0.000000 1.000000 0.179953 0.147689 0.987631 223 9.378 0.011285 0.000003 0.403390 0.825144 0.174155 224 6.778 0.000000 0.003992 0.475817 0.046096 0.625105 225 10.179 0.001517 0.000113 0.773401 0.179212 0.071167 226 8.852 1.000000 0.000000 0.042709 0.908040 0.098544 227 8.619 0.000016 0.001723 0.476723 0.985358 0.563742 228 6.921 0.000333 0.000431 0.308861 0.006345 0.426642 229 5.099 1.000000 0.000000 0.235142 0.050177 0.001077 230 4.070 0.000128 1.000000 0.008735 0.013543 0.852098 231 9.247 1.000000 0.000001 0.723148 0.952752 0.562936 232 6.059 1.000000 0.638753 0.000001 0.000000 0.869569 233 8.425 0.009739 0.003879 0.998629 0.495878 0.558822 234 7.802 0.000086 0.879292 0.116376 0.451548 0.008324 235 8.749 0.908521 0.000000 0.439119 0.594489 0.101186 236 7.040 0.000011 0.001007 0.386796 0.919567 0.226387 237 3.647 0.002951 1.000000 0.000262 0.000208 0.874090 238 10.276 1.000000 0.000000 0.418422 0.832036 0.186944 239 10.069 0.084230 0.126068 0.777086 0.005812 0.101705 240 9.473 1.000000 0.000001 0.408420 0.488688 0.954477 241 9.310 0.018867 0.000017 0.811822 0.177187 0.617435 242 3.257 0.713444 0.000000 0.455516 0.068219 0.006227 243 5.837 0.011176 1.000000 0.000486 0.005152 0.486482 244 6.063 1.000000 0.000000 0.009609 0.587740 0.085624 245 4.692 0.000031 1.000000 0.034824 0.206071 0.549376 246 8.101 1.000000 0.003242 0.018153 0.000001 0.136854 247 7.473 0.000001 1.000000 0.732308 0.247163 0.086327 248 8.061 0.053345 0.004214 0.630998 0.065841 0.013683 249 6.219 0.000014 0.000574 0.023214 0.699643 0.121152 250 7.253 0.906687 0.000002 0.263662 0.846643 0.520142 251 4.826 0.000004 1.000000 0.578201 0.004767 0.174039 252 9.701 1.000000 0.000332 0.072707 0.013999 0.962410 253 10.228 1.000000 0.000007 0.087003 0.967489 0.053514 254 9.763 0.000001 0.017414 0.905037 0.337602 0.704183 255 6.237 1.000000 0.000001 0.744162 0.779976 0.389990 256 5.498 0.000172 0.002470 0.224230 0.119996 0.998003 257 9.296 0.000000 1.000000 0.790640 0.575586 0.284504 258 9.361 0.001998 0.007361 0.085034 0.976056 0.056129 259 6.933 0.000121 1.000000 0.035314 0.917808 0.079681 260 8.146 0.000817 1.000000 0.003077 0.175728 0.168438 261 10.387 1.000000 0.000000 0.154204 0.005202 0.633454 262 7.322 1.000000 0.000000 0.184100 0.642671 0.032921 263 8.937 1.000000 0.000000 0.270540 0.411394 0.024808 264 8.099 0.000006 0.005941 0.629532 0.232135 0.868727 265 8.658 0.001307 0.000125 0.042189 0.642870 0.220174 266 7.307 0.136406 0.003979 0.899378 0.062794 0.276354 267 7.948 0.089409 0.021345 0.534724 0.441022 0.999958 268 4.814 0.188367 0.002418 0.307760 0.984538 0.242328 269 8.262 0.003309 0.000013 0.050366 0.644219 0.248933 270 6.153 0.263893 0.011698 0.965100 0.195596 0.424691 271 6.929 0.004682 1.000000 0.000051 0.739871 0.000534 272 8.747 0.000009 1.000000 0.661459 0.056585 0.485502 273 7.670 0.002863 0.002878 0.963051 0.489433 0.425727 274 9.060 0.000004 0.006105 0.996920 0.309691 0.456795 275 7.741 0.000237 0.794307 0.149393 0.868630 0.057952 276 6.472 0.556174 0.000261 0.038526 0.798163 0.134791 277 9.866 1.000000 0.000006 0.557046 0.887942 0.802758 278 5.372 1.000000 0.438013 0.000033 0.171523 0.007397 279 9.620 0.000424 0.113905 0.627252 0.847118 0.345974 280 7.539 0.001516 1.000000 0.250568 0.355397 0.913465 281 7.760 1.000000 0.245460 0.750146 0.000037 0.004098 282 6.359 0.156285 1.000000 0.000226 0.002251 0.494438 283 4.554 1.000000 0.000000 0.122703 0.982429 0.088234 284 6.914 0.000011 1.000000 0.027018 0.806772 0.098170 285 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 286 7.625 1.000000 0.002245 0.254101 0.080350 0.953292 287 7.088 1.000000 0.000005 0.953122 0.639431 0.530295 288 7.671 0.789194 1.000000 0.000012 0.023331 0.029137 289 11.703 0.162049 0.000001 0.862749 0.969968 0.947024 290 11.692 0.064067 1.000000 0.011327 0.010003 0.932084 291 8.784 0.001733 0.264252 0.097918 0.824211 0.261542 292 8.527 0.163494 0.002054 0.980872 0.858346 0.960862 293 6.253 0.000028 1.000000 0.065034 0.016357 0.984841 294 7.716 1.000000 0.000000 0.999922 0.538365 0.635314 295 8.239 1.000000 1.000000 0.000120 0.001571 0.448676 296 9.702 0.000005 1.000000 0.830277 0.934225 0.650508 297 6.644 1.000000 0.000023 0.471520 0.309485 0.994504 298 8.283 1.000000 0.001039 0.155603 0.877005 0.063053 299 9.230 1.000000 0.000100 0.078833 0.793766 0.239952 300 8.065 1.000000 0.000005 0.095936 0.867921 0.226701 301 7.113 1.000000 0.001735 0.115620 0.000618 0.379268 302 5.954 0.000000 1.000000 0.186587 0.721925 0.044203 303 7.810 1.000000 1.000000 0.160174 0.000219 0.191966 304 6.772 1.000000 0.748516 0.000612 0.000014 0.929559 305 6.056 0.010741 0.032666 0.488873 0.253434 0.969967 306 8.840 0.014281 0.064555 0.679691 0.009289 0.184703 307 8.540 1.000000 1.000000 0.000906 0.007024 0.542344 308 7.221 0.000572 1.000000 0.238268 0.690136 0.055163 309 6.026 1.000000 0.000002 0.138073 0.999915 0.135136 310 8.523 0.000049 0.377958 0.469588 0.921733 0.676477 311 6.488 0.001154 1.000000 0.779960 0.773039 0.418346 312 6.408 1.000000 1.000000 0.007884 0.000183 0.836520 313 7.023 1.000000 0.000000 0.162844 0.999656 0.156176 314 4.576 1.000000 0.188292 0.144998 0.026471 0.921677 315 7.764 1.000000 0.000026 0.453417 0.841420 0.752519 316 7.620 1.000000 0.000002 0.977232 0.934372 0.994545 317 5.666 1.000000 0.000010 0.537664 0.982363 0.443725 318 8.672 1.000000 0.000532 0.865968 0.779044 0.519467 319 4.419 1.000000 0.000000 0.416052 0.694594 0.124264

Example 6 Ratios of IRS Biomarkersmarkers Between Healthy, inSIRS, Mild Sepsis, Severe Sepsis and Septic Shock

Examples of the use of 2-gene ratios as a more informative predictor of clinical condition than either of the two component genes are presented in Tables 16, 17, 18, 19, 20 and 21. These tables show instances of the prediction of Healthy and inSIRS (Table 16), Healthy vs. ipSIRS (Table 17), inSIRS and ipSIRS (Table 18), Mild Sepsis vs. Vs Severe Sepsis (Table 19), Mild Sepsis Vs Septic Shock (Table 20), and Severe Sepsis vs. Vs Septic Shock (Table 21) using 2 genes and their ratios. Columns from left to right are: name of the first component gene (Gene 1 Name), the corresponding Area Under Curve for this gene (Gene 1 AUC), the second component gene (Gene 2 Name), the corresponding AUC for this gene (Gene 2 AUC), the AUC for this ratio (Ratio AUC), the statistical significance using Delong's method (DeLong E R, DeLong D M, Clarke-Pearson D L: Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Non parametric Approach. Biometrics 1988, 44:837-845) that the ratio is a better predictor than Gene 1 (Ratio Signif to Gene 1), the statistical significance using Delong's method that the ratio is a better predictor than Genet (Ratio Signif to Gene 2). These tables show results for which the ratio AUC is shown to be superior to both of the component genes, and the improvement statistically significant over both genes. Examples of less significant ratios, or cases where the ratio is statistically superior to only one of the component genes are not listed in these tables. Such ratios can also be used in clinical trials in a similar fashion to that described in Example 5.

TABLE 16 Ratios Healthy Versus inSIRS Gene 1 Name Gene 1 AUC Gene 2 Name Gene 2 AUC Ratio AUC Ratio Signif to Gene 1 Ratio Signif to Gene 2 TLR5 0.874 HLA-DPB1 0.815 0.953 0.008 0.004 TLR5 0.874 PLAC8 0.828 0.979 0.003 0.004 TLR5 0.874 CDS2 0.73 0.933 0.003 0.003 TLR5 0.874 SLC39A9 0.776 0.946 0.003 0.002 TLR5 0.874 LGALS1 0.722 0.941 0.015 0 TLR5 0.874 DNAJC9_FAM149B1 0.944 0.994 0.003 0.037 TLR5 0.874 LGALS2 0.922 0.983 0.003 0.019 TLR5 0.874 HINT1 0.9 0.949 0.048 0.036 TLR5 0.874 HS2ST1_UBA2 0.668 0.938 0.006 0 TLR5 0.874 HLA-DPA1 0.796 0.948 0.005 0.003 TLR5 0.874 PTGER2 0.881 0.957 0.007 0.038 TLR5 0.874 TRAF3 0.87 0.966 0.011 0.015 TLR5 0.874 RPL17_SNORD58B 0.917 0.964 0.02 0.029 TLR5 0.874 JUP 0.753 0.938 0.02 0.007 TLR5 0.874 HSP90AB1_HSP90A 0.924 0.991 0.003 0.021 TLR5 0.874 ITGA4_CERKL 0.88 0.957 0.018 0.026 TLR5 0.874 EIF1AX_SCARNA9L 0.871 0.942 0.039 0.032 TLR5 0.874 CD300A 0.841 0.955 0.003 0.02 TLR5 0.874 P4HA1_RPL17 0.664 0.947 0.027 0 TLR5 0.874 MGST3 0.857 0.969 0.002 0.011 TLR5 0.874 KIAA0746 0.776 0.949 0.026 0.001 TLR5 0.874 HSPB1_HSPBL2 0.705 0.915 0.05 0.001 TLR5 0.874 CD151 0.718 0.938 0.047 0 TLR5 0.874 IGK@_IGKC_IGKV 0.855 0.946 0.01 0.02 TLR5 0.874 SON 0.856 0.942 0.02 0.045 TLR5 0.874 IRF1 0.82 0.961 0.003 0.008 TLR5 0.874 C7orf58 0.831 0.996 0.003 0 TLR5 0.874 NP 0.726 0.957 0.021 0 TLR5 0.874 AIF1 0.618 0.941 0.019 0 TLR5 0.874 CDC26 0.799 0.949 0.009 0.003 TLR5 0.874 TAS2R31 0.833 0.946 0.038 0.002 TLR5 0.874 NFXL1 0.728 0.945 0.017 0 TLR5 0.874 DCTN5 0.722 0.929 0.034 0 UBE2J1 0.888 FAIM3 0.93 0.981 0.007 0.023 UBE2J1 0.888 SH2D1B 0.894 0.953 0.034 0.02 UBE2J1 0.888 LRRN3 0.928 0.962 0.029 0.023 UBE2J1 0.888 HLA-DPB1 0.815 0.974 0.015 0 UBE2J1 0.888 PLAC8 0.828 0.997 0.002 0.001 UBE2J1 0.888 GOT2 0.966 0.99 0.004 0.044 UBE2J1 0.888 CDS2 0.73 0.958 0.005 0 UBE2J1 0.888 SLC39A9 0.776 0.981 0.002 0 UBE2J1 0.888 GIMAP7 0.804 0.967 0.034 0 UBE2J1 0.888 INSIG1 0.957 0.997 0.002 0.032 UBE2J1 0.888 LGALS1 0.722 0.946 0.046 0 UBE2J1 0.888 DNAJC9_FAM149B1 0.944 0.992 0.005 0.017 UBE2J1 0.888 LGALS2 0.922 0.979 0.004 0.015 UBE2J1 0.888 HS2ST1_UBA2 0.668 0.956 0.032 0 UBE2J1 0.888 HLA-DPA1 0.796 0.971 0.015 0 UBE2J1 0.888 PTGER2 0.881 0.978 0.007 0.002 UBE2J1 0.888 PMS2CL_PMS2 0.942 0.992 0.003 0.017 UBE2J1 0.888 NEK6_LOC1001290 0.671 0.948 0.047 0 UBE2J1 0.888 TRAF3 0.87 0.981 0.011 0.003 UBE2J1 0.888 IRF4 0.946 0.993 0.002 0.041 UBE2J1 0.888 AMFR 0.867 0.947 0.049 0.025 UBE2J1 0.888 HSP90AB1_HSP90A 0.924 0.996 0.003 0.009 UBE2J1 0.888 ITGA4_CERKL 0.88 0.987 0.005 0.002 UBE2J1 0.888 EIF1AX_SCARNA9L 0.871 0.971 0.023 0.001 UBE2J1 0.888 CD300A 0.841 0.964 0.002 0.005 UBE2J1 0.888 MGST3 0.857 0.98 0.007 0.001 UBE2J1 0.888 KIAA0746 0.776 0.964 0.05 0 UBE2J1 0.888 CCR4 0.871 0.971 0.018 0.004 UBE2J1 0.888 RRP12_LOC644215 0.904 0.976 0.006 0.015 UBE2J1 0.888 TIMM10 0.949 0.992 0.002 0.031 UBE2J1 0.888 FAM118A 0.882 0.976 0.009 0.01 UBE2J1 0.888 IGK@_IGKC_IGKV 0.855 0.962 0.039 0.002 UBE2J1 0.888 SON 0.856 0.967 0.023 0.006 UBE2J1 0.888 IRF1 0.82 0.965 0.004 0.002 UBE2J1 0.888 C7orf58 0.831 0.988 0.007 0 UBE2J1 0.888 B4GALT3 0.917 0.975 0.006 0.041 UBE2J1 0.888 CDC26 0.799 0.956 0.029 0 UBE2J1 0.888 DCTN5 0.722 0.955 0.042 0 RNASE2_LOC64333 0.855 HLA-DPB1 0.815 0.955 0.009 0.004 RNASE2_LOC64333 0.855 PLAC8 0.828 0.969 0.003 0.009 RNASE2_LOC64333 0.855 ITK 0.874 0.94 0.034 0.047 RNASE2_LOC64333 0.855 CDS2 0.73 0.912 0.01 0.008 RNASE2_LOC64333 0.855 CCR3 0.869 0.95 0.008 0.047 RNASE2_LOC64333 0.855 SLC39A9 0.776 0.944 0.003 0.003 RNASE2_LOC64333 0.855 GIMAP7 0.804 0.951 0.009 0.003 RNASE2_LOC64333 0.855 LGALS1 0.722 0.952 0.003 0.001 RNASE2_LOC64333 0.855 HLA-DRA 0.855 0.97 0.005 0.012 RNASE2_LOC64333 0.855 SIAE 0.737 0.921 0.018 0.01 RNASE2_LOC64333 0.855 HIST1H3J 0.898 0.949 0.007 0.021 RNASE2_LOC64333 0.855 MTRR 0.674 0.946 0.018 0 RNASE2_LOC64333 0.855 HIST1H3A 0.908 0.961 0.009 0.015 RNASE2_LOC64333 0.855 HLA-DPA1 0.796 0.951 0.005 0.004 RNASE2_LOC64333 0.855 SLC39A8 0.825 0.954 0.007 0.021 RNASE2_LOC64333 0.855 PTGER2 0.881 0.954 0.005 0.035 RNASE2_LOC64333 0.855 NEK6_LOC1001290 0.671 0.921 0.02 0.001 RNASE2_LOC64333 0.855 TRAF3 0.87 0.97 0.003 0.033 RNASE2_LOC64333 0.855 JUP 0.753 0.931 0.025 0.011 RNASE2_LOC64333 0.855 GALNT2 0.528 0.898 0.017 0 RNASE2_LOC64333 0.855 HSP90AB1_HSP90A 0.924 0.988 0.002 0.036 RNASE2_LOC64333 0.855 GLT25D1 0.77 0.956 0.005 0.004 RNASE2_LOC64333 0.855 ITGA4_CERKL 0.88 0.958 0.007 0.03 RNASE2_LOC64333 0.855 EIF1AX_SCARNA9L 0.871 0.958 0.009 0.011 RNASE2_LOC64333 0.855 ZNF28 0.837 0.921 0.023 0.026 RNASE2_LOC64333 0.855 ICAM1 0.675 0.931 0.013 0 RNASE2_LOC64333 0.855 P4HA1_RPL17 0.664 0.935 0.034 0 RNASE2_LOC64333 0.855 KLHL5 0.657 0.924 0.034 0 RNASE2_LOC64333 0.855 MGST3 0.857 0.97 0.002 0.01 RNASE2_LOC64333 0.855 KIAA0746 0.776 0.945 0.018 0.001 RNASE2_LOC64333 0.855 HSPB1_HSPBL2 0.705 0.913 0.009 0.004 RNASE2_LOC64333 0.855 CCR4 0.871 0.947 0.012 0.047 RNASE2_LOC64333 0.855 HIST1H3B 0.89 0.948 0.013 0.045 RNASE2_LOC64333 0.855 FSD1L_GARNL1 0.779 0.929 0.013 0.008 RNASE2_LOC64333 0.855 CD151 0.718 0.923 0.039 0 RNASE2_LOC64333 0.855 HIST1H3H 0.861 0.919 0.05 0.021 RNASE2_LOC64333 0.855 IGK@_IGKC_IGKV 0.855 0.948 0.013 0.015 RNASE2_LOC64333 0.855 IRF1 0.82 0.926 0.012 0.03 RNASE2_LOC64333 0.855 C7orf58 0.831 0.978 0.006 0.001 RNASE2_LOC64333 0.855 CTSL1_CTSLL3 0.676 0.912 0.042 0 RNASE2_LOC64333 0.855 NP 0.726 0.941 0.017 0 RNASE2_LOC64333 0.855 TMEM62_SPCS2_L 0.78 0.937 0.036 0.002 RNASE2_LOC64333 0.855 TAS2R31 0.833 0.945 0.026 0.002 RNASE2_LOC64333 0.855 DCTN5 0.722 0.925 0.021 0 C3AR1 0.868 GPR56 0.943 0.98 0.009 0.032 C3AR1 0.868 CAMK4 0.921 0.97 0.038 0.034 C3AR1 0.868 SH2D1B 0.894 0.974 0.014 0.003 C3AR1 0.868 HLA-DPB1 0.815 0.935 0.025 0.023 C3AR1 0.868 VAMP2 0.783 0.929 0.029 0.01 C3AR1 0.868 PLAC8 0.828 0.969 0.004 0.018 C3AR1 0.868 ITK 0.874 0.961 0.049 0.008 C3AR1 0.868 SLC39A9 0.776 0.953 0.008 0.004 C3AR1 0.868 GIMAP7 0.804 0.957 0.013 0.002 C3AR1 0.868 LGALS1 0.722 0.928 0.019 0.004 C3AR1 0.868 DNAJC9_FAM149B1 0.944 0.997 0.006 0.026 C3AR1 0.868 SIAE 0.737 0.927 0.042 0.007 C3AR1 0.868 HINT1 0.9 0.965 0.038 0.01 C3AR1 0.868 HLA-DPA1 0.796 0.931 0.025 0.014 C3AR1 0.868 PTGER2 0.881 0.962 0.006 0.044 C3AR1 0.868 PMS2CL_PMS2 0.942 0.992 0.005 0.036 C3AR1 0.868 RPL17_SNORD58B 0.917 0.965 0.033 0.015 C3AR1 0.868 GALNT2 0.528 0.91 0.024 0 C3AR1 0.868 GLT25D1 0.77 0.937 0.01 0.021 C3AR1 0.868 ITGA4_CERKL 0.88 0.967 0.014 0.021 C3AR1 0.868 EIF1AX_SCARNA9L 0.871 0.956 0.035 0.008 C3AR1 0.868 P4HA1_RPL17 0.664 0.942 0.044 0 C3AR1 0.868 KIAA0746 0.776 0.948 0.038 0.001 C3AR1 0.868 FSD1L_GARNL1 0.779 0.928 0.024 0.013 C3AR1 0.868 IGK@_IGKC_IGKV 0.855 0.951 0.035 0.025 C3AR1 0.868 SON 0.856 0.961 0.025 0.012 C3AR1 0.868 C7orf58 0.831 0.958 0.019 0.003 C3AR1 0.868 NP 0.726 0.942 0.016 0.001 C3AR1 0.868 CDC26 0.799 0.934 0.018 0.02 C3AR1 0.868 TAS2R31 0.833 0.947 0.046 0.003 C3AR1 0.868 DCTN5 0.722 0.94 0.017 0.001 GPR56 0.943 TGFBR1 0.734 0.984 0.007 0 GPR56 0.943 DNAJC13 0.819 0.985 0.03 0 GPR56 0.943 ANKRD34B 0.904 0.987 0.037 0.002 GPR56 0.943 SGMS2 0.938 0.996 0.013 0.013 GPR56 0.943 B3GNT5_MCF2L2 0.949 0.987 0.018 0.026 GPR56 0.943 SMPDL3A 0.797 0.981 0.027 0 GPR56 0.943 LOC284757 0.939 0.996 0.013 0.048 GPR56 0.943 PPP2R5A_SNORA16 0.953 0.997 0.009 0.029 GPR56 0.943 FRMD3 0.755 0.98 0.011 0 GPR56 0.943 EIF2AK2 0.964 0.999 0.011 0.034 GPR56 0.943 NA 0.868 0.977 0.043 0.002 GPR56 0.943 HIST1H2AA 0.903 0.978 0.028 0.012 GPR56 0.943 CDA 0.945 0.978 0.03 0.045 GPR56 0.943 SAP30 0.911 0.983 0.029 0.021 GPR56 0.943 ACPL2 0.906 0.99 0.018 0.011 GPR56 0.943 HAL 0.942 0.99 0.018 0.018 GPR56 0.943 KIAA1257_ACAD9/ 0.851 0.981 0.031 0.001 GPR56 0.943 CCDC125 0.917 0.995 0.011 0.009 GPR56 0.943 PDK4 0.863 0.987 0.041 0.001 GPR56 0.943 ZRANB1 0.897 0.99 0.023 0.002 GPR56 0.943 MPZL3 0.892 0.975 0.017 0.007 GPR56 0.943 CYP4F3_CYP4F2 0.901 0.995 0.024 0.007 GPR56 0.943 SYNE2 0.748 0.997 0.013 0 GPR56 0.943 MME 0.894 0.986 0.043 0.007 GPR56 0.943 RBP7 0.848 0.974 0.035 0.002 GPR56 0.943 RGS2 0.915 0.979 0.01 0.02 FCGR1A_FCGR1B_(—) 0.87 HLA-DPB1 0.815 0.952 0.002 0.008 FCGR1A_FCGR1B_(—) 0.87 VAMP2 0.783 0.895 0.029 0.038 FCGR1A_FCGR1B_(—) 0.87 CASS4 0.738 0.91 0.024 0.003 FCGR1A_FCGR1B_(—) 0.87 PLAC8 0.828 0.971 0.002 0.009 FCGR1A_FCGR1B_(—) 0.87 ITK 0.874 0.949 0.013 0.041 FCGR1A_FCGR1B_(—) 0.87 CDS2 0.73 0.916 0.004 0.008 FCGR1A_FCGR1B_(—) 0.87 SLC39A9 0.776 0.932 0.002 0.007 FCGR1A_FCGR1B_(—) 0.87 GIMAP7 0.804 0.956 0.004 0.002 FCGR1A_FCGR1B_(—) 0.87 LGALS1 0.722 0.933 0.009 0.001 FCGR1A_FCGR1B_(—) 0.87 FFAR2 0.637 0.934 0.014 0 FCGR1A_FCGR1B_(—) 0.87 LGALS2 0.922 0.986 0.002 0.017 FCGR1A_FCGR1B_(—) 0.87 MTRR 0.674 0.947 0.006 0 FCGR1A_FCGR1B_(—) 0.87 HS2ST1_UBA2 0.668 0.922 0.015 0 FCGR1A_FCGR1B_(—) 0.87 HLA-DPA1 0.796 0.932 0.002 0.018 FCGR1A_FCGR1B_(—) 0.87 SLC39A8 0.825 0.95 0.001 0.041 FCGR1A_FCGR1B_(—) 0.87 NEK6_LOC1001290 0.671 0.907 0.035 0.001 FCGR1A_FCGR1B_(—) 0.87 JUP 0.753 0.936 0.012 0.011 FCGR1A_FCGR1B_(—) 0.87 GALNT2 0.528 0.897 0.026 0 FCGR1A_FCGR1B_(—) 0.87 GLT25D1 0.77 0.933 0.024 0.004 FCGR1A_FCGR1B_(—) 0.87 HDHD1A 0.602 0.914 0.028 0 FCGR1A_FCGR1B_(—) 0.87 EIF1AX_SCARNA9L 0.871 0.965 0.003 0.017 FCGR1A_FCGR1B_(—) 0.87 SFRS9 0.558 0.901 0.039 0 FCGR1A_FCGR1B_(—) 0.87 ZNF28 0.837 0.922 0.029 0.048 FCGR1A_FCGR1B_(—) 0.87 ICAM1 0.675 0.96 0.002 0 FCGR1A_FCGR1B_(—) 0.87 P4HA1_RPL17 0.664 0.942 0.006 0 FCGR1A_FCGR1B_(—) 0.87 KLHL5 0.657 0.933 0.022 0 FCGR1A_FCGR1B_(—) 0.87 MGST3 0.857 0.97 0.002 0.009 FCGR1A_FCGR1B_(—) 0.87 KIAA0746 0.776 0.952 0.012 0.001 FCGR1A_FCGR1B_(—) 0.87 HSPB1_HSPBL2 0.705 0.91 0.018 0.003 FCGR1A_FCGR1B_(—) 0.87 CCR4 0.871 0.964 0.002 0.036 FCGR1A_FCGR1B_(—) 0.87 IL1B 0.576 0.977 0.012 0 FCGR1A_FCGR1B_(—) 0.87 FSD1L_GARNL1 0.779 0.908 0.044 0.01 FCGR1A_FCGR1B_(—) 0.87 NA 0.7 0.95 0.02 0 FCGR1A_FCGR1B_(—) 0.87 IGK@_IGKC_IGKV 0.855 0.941 0.01 0.028 FCGR1A_FCGR1B_(—) 0.87 IRF1 0.82 0.95 0.001 0.021 FCGR1A_FCGR1B_(—) 0.87 C7orf58 0.831 0.983 0.003 0.001 FCGR1A_FCGR1B_(—) 0.87 NP 0.726 0.932 0.033 0.001 FCGR1A_FCGR1B_(—) 0.87 PLEKHA3 0.645 0.919 0.025 0 FCGR1A_FCGR1B_(—) 0.87 CDC26 0.799 0.919 0.02 0.024 FCGR1A_FCGR1B_(—) 0.87 TAS2R31 0.833 0.961 0.003 0.003 FCGR1A_FCGR1B_(—) 0.87 IGLV6-57 0.761 0.914 0.044 0.002 FCGR1A_FCGR1B_(—) 0.87 NFXL1 0.728 0.929 0.019 0 FCGR1A_FCGR1B_(—) 0.87 DCTN5 0.722 0.932 0.005 0.001 C11orf82 0.58 C7orf58 0.831 0.911 0 0.023 FAR2 0.843 PTGDR 0.896 0.953 0.014 0.035 FAR2 0.843 SH2D1B 0.894 0.961 0.007 0.006 FAR2 0.843 VAMP2 0.783 0.936 0.005 0.006 FAR2 0.843 SLC39A9 0.776 0.921 0.013 0.004 FAR2 0.843 NEK6_LOC1001290 0.671 0.893 0.046 0.001 FAR2 0.843 GLT25D1 0.77 0.912 0.016 0.027 FAR2 0.843 ITGA4_CERKL 0.88 0.955 0.011 0.013 FAR2 0.843 EIF1AX_SCARNA9L 0.871 0.939 0.028 0.01 FAR2 0.843 CD300A 0.841 0.946 0.001 0.02 FAR2 0.843 ZNF28 0.837 0.934 0.021 0.006 FAR2 0.843 ABCG1 0.85 0.952 0.001 0.037 FAR2 0.843 HIST1H3B 0.89 0.946 0.016 0.037 FAR2 0.843 FSD1L_GARNL1 0.779 0.921 0.006 0.007 FAR2 0.843 APOLD1 0.871 0.956 0.003 0.021 FAR2 0.843 IGK@_IGKC_IGKV 0.855 0.926 0.031 0.024 FAR2 0.843 SON 0.856 0.948 0.009 0.024 FAR2 0.843 IRF1 0.82 0.921 0.006 0.044 FAR2 0.843 TNFRSF17 0.875 0.935 0.021 0.048 FAR2 0.843 CDC26 0.799 0.935 0.003 0.008 GNLY 0.914 GALNT3 0.837 0.97 0.036 0.001 GNLY 0.914 STOM 0.825 0.952 0.02 0.003 GNLY 0.914 TGFBR1 0.734 0.958 0.01 0 GNLY 0.914 DNAJC13 0.819 0.961 0.03 0.001 GNLY 0.914 ANKRD34B 0.904 0.964 0.019 0.035 GNLY 0.914 SGMS2 0.938 0.989 0.006 0.028 GNLY 0.914 C9orf72 0.796 0.959 0.028 0.001 GNLY 0.914 SMPDL3A 0.797 0.977 0.007 0.001 GNLY 0.914 PPP2R5A_SNORA16 0.953 0.993 0.005 0.044 GNLY 0.914 FRMD3 0.755 0.955 0.018 0 GNLY 0.914 NA 0.927 0.99 0.01 0.012 GNLY 0.914 EIF2AK2 0.964 0.997 0.005 0.048 GNLY 0.914 NA 0.868 0.967 0.027 0.003 GNLY 0.914 ACPL2 0.906 0.978 0.014 0.029 GNLY 0.914 OCR1 0.896 0.977 0.046 0.009 GNLY 0.914 CCDC125 0.917 0.988 0.004 0.017 GNLY 0.914 ZRANB1 0.897 0.989 0.008 0.004 GNLY 0.914 CYP4F3_CYP4F2 0.901 0.981 0.045 0.007 GNLY 0.914 SYNE2 0.748 0.973 0.019 0 GNLY 0.914 RBP7 0.848 0.976 0.013 0.002 GALNT3 0.837 FAIM3 0.93 0.983 0 0.032 GALNT3 0.837 PTGDR 0.896 0.964 0.002 0.024 GALNT3 0.837 CAMK4 0.921 0.98 0.001 0.033 GALNT3 0.837 SH2D1B 0.894 0.976 0.001 0.003 GALNT3 0.837 TLR10 0.521 0.942 0.031 0 GALNT3 0.837 CRIP1 0.943 0.957 0.004 0.05 GALNT3 0.837 LRRN3 0.928 0.974 0.001 0.017 GALNT3 0.837 HLA-DPB1 0.815 0.934 0.005 0.007 GALNT3 0.837 CASS4 0.738 0.92 0.008 0.001 GALNT3 0.837 KPNA5 0.895 0.98 0.001 0.005 GALNT3 0.837 PLAC8 0.828 0.985 0.001 0.001 GALNT3 0.837 KLRK1_KLRC4 0.865 0.948 0.018 0.006 GALNT3 0.837 ITK 0.874 0.969 0.003 0.006 GALNT3 0.837 FGFBP2 0.814 0.937 0.015 0.001 GALNT3 0.837 CDS2 0.73 0.925 0 0.004 GALNT3 0.837 CCR3 0.869 0.937 0.014 0.003 GALNT3 0.837 CHI3L1 0.722 0.911 0.03 0.001 GALNT3 0.837 SLC39A9 0.776 0.958 0 0 GALNT3 0.837 GIMAP7 0.804 0.982 0 0 GALNT3 0.837 LGALS1 0.722 0.901 0.009 0.003 GALNT3 0.837 DNAJC9_FAM149B1 0.944 0.989 0 0.024 GALNT3 0.837 LGALS2 0.922 0.972 0 0.046 GALNT3 0.837 SIAE 0.737 0.927 0.002 0.003 GALNT3 0.837 HINT1 0.9 0.965 0.002 0.017 GALNT3 0.837 MTRR 0.674 0.954 0.003 0 GALNT3 0.837 AIG1 0.56 0.921 0.023 0 GALNT3 0.837 HS2ST1_UBA2 0.668 0.973 0 0 GALNT3 0.837 HLA-DPA1 0.796 0.939 0.003 0.002 GALNT3 0.837 PTGER2 0.881 0.969 0 0.016 GALNT3 0.837 PMS2CL_PMS2 0.942 0.994 0 0.028 GALNT3 0.837 NEK6_LOC1001290 0.671 0.899 0.039 0 GALNT3 0.837 CENPK 0.834 0.942 0.023 0.001 GALNT3 0.837 TRAF3 0.87 0.971 0.002 0.004 GALNT3 0.837 RPL17_SNORD58B 0.917 0.978 0.001 0.01 GALNT3 0.837 HSP90AB1_HSP90A 0.924 0.988 0 0.009 GALNT3 0.837 GLT25D1 0.77 0.905 0.02 0.009 GALNT3 0.837 ITGA4_CERKL 0.88 0.993 0 0.004 GALNT3 0.837 EIF1AX_SCARNA9L 0.871 0.976 0.001 0.002 GALNT3 0.837 CD300A 0.841 0.946 0 0.03 GALNT3 0.837 ZNF28 0.837 0.946 0.003 0.005 GALNT3 0.837 P4HA1_RPL17 0.664 0.956 0.006 0 GALNT3 0.837 KLHL5 0.657 0.971 0.001 0 GALNT3 0.837 MGST3 0.857 0.964 0.001 0.002 GALNT3 0.837 KIAA0746 0.776 0.949 0.012 0 GALNT3 0.837 CCR4 0.871 0.99 0 0.002 GALNT3 0.837 FSD1L_GARNL1 0.779 0.916 0.009 0.003 GALNT3 0.837 FAM118A 0.882 0.965 0 0.042 GALNT3 0.837 CD151 0.718 0.901 0.048 0.001 GALNT3 0.837 HIST1H3H 0.861 0.914 0.047 0.029 GALNT3 0.837 PYHIN1 0.686 0.926 0.044 0 GALNT3 0.837 IGK@_IGKC_IGKV 0.855 0.964 0.002 0.002 GALNT3 0.837 IRF1 0.82 0.953 0 0.01 GALNT3 0.837 C7orf58 0.831 0.987 0.001 0 GALNT3 0.837 B4GALT3 0.917 0.99 0 0.035 GALNT3 0.837 NP 0.726 0.976 0 0 GALNT3 0.837 C4orf3 0.723 0.891 0.032 0.002 GALNT3 0.837 TNFRSF17 0.875 0.944 0.005 0.024 GALNT3 0.837 CDC26 0.799 0.938 0.001 0.005 GALNT3 0.837 TAS2R31 0.833 0.96 0.003 0.001 GALNT3 0.837 NFXL1 0.728 0.942 0.014 0 GALNT3 0.837 DCTN5 0.722 0.925 0.014 0 OMG 0.851 SH2D1B 0.894 0.951 0.005 0.03 OMG 0.851 LRRN3 0.928 0.955 0.015 0.047 OMG 0.851 CDS2 0.73 0.895 0.044 0.004 OMG 0.851 HIST1H4L 0.961 0.996 0 0.042 OMG 0.851 LGALS1 0.722 0.91 0.013 0.001 OMG 0.851 LGALS2 0.922 0.972 0.001 0.036 OMG 0.851 HIST1H3J 0.898 0.961 0.005 0.006 OMG 0.851 HS2ST1_UBA2 0.668 0.897 0.047 0.001 OMG 0.851 HIST1H3A 0.908 0.971 0.006 0.004 OMG 0.851 RPL17_SNORD58B 0.917 0.971 0.002 0.035 OMG 0.851 GLT25D1 0.77 0.913 0.032 0.014 OMG 0.851 ITGA4_CERKL 0.88 0.946 0.005 0.031 OMG 0.851 EIF1AX_SCARNA9L 0.871 0.94 0.022 0.017 OMG 0.851 ZNF28 0.837 0.957 0.005 0.002 OMG 0.851 MGST3 0.857 0.921 0.011 0.039 OMG 0.851 HIST1H3B 0.89 0.969 0.004 0.01 OMG 0.851 FSD1L_GARNL1 0.779 0.924 0.018 0.004 OMG 0.851 HIST1H3H 0.861 0.958 0.014 0.001 OMG 0.851 APOLD1 0.871 0.964 0.001 0.016 OMG 0.851 IRF1 0.82 0.909 0.026 0.047 OMG 0.851 CDC26 0.799 0.947 0.003 0.003 STOM 0.825 CAMK4 0.921 0.94 0.014 0.046 STOM 0.825 SH2D1B 0.894 0.938 0.016 0.004 STOM 0.825 KPNA5 0.895 0.929 0.028 0.013 STOM 0.825 PLAC8 0.828 0.912 0.048 0.014 STOM 0.825 HIST1H4E 0.915 0.975 0 0.04 STOM 0.825 CCR3 0.869 0.906 0.037 0.017 STOM 0.825 HIST1H4L 0.961 0.992 0 0.018 STOM 0.825 HINT1 0.9 0.934 0.015 0.043 STOM 0.825 HIST1H3J 0.898 0.962 0.004 0.002 STOM 0.825 HIST1H3A 0.908 0.962 0.003 0.004 STOM 0.825 PTGER2 0.881 0.939 0.002 0.032 STOM 0.825 AMFR 0.867 0.966 0 0.013 STOM 0.825 RPL17_SNORD58B 0.917 0.957 0.007 0.03 STOM 0.825 EIF1AX_SCARNA9L 0.871 0.92 0.03 0.011 STOM 0.825 CD300A 0.841 0.909 0.009 0.039 STOM 0.825 ZNF28 0.837 0.923 0.026 0.005 STOM 0.825 MGST3 0.857 0.939 0.003 0.012 STOM 0.825 TYMS 0.863 0.961 0.001 0.005 STOM 0.825 HIST1H2BM 0.891 0.969 0.001 0.01 STOM 0.825 HIST1H3B 0.89 0.96 0.003 0.004 STOM 0.825 FAM118A 0.882 0.963 0.002 0.008 STOM 0.825 HIST1H3H 0.861 0.944 0.017 0 STOM 0.825 APOLD1 0.871 0.949 0.001 0.015 STOM 0.825 IGK@_IGKC_IGKV 0.855 0.912 0.044 0.008 STOM 0.825 SON 0.856 0.921 0.045 0.011 STOM 0.825 IRF1 0.82 0.911 0.016 0.025 STOM 0.825 B4GALT3 0.917 0.986 0 0.021 STOM 0.825 TNFRSF17 0.875 0.918 0.03 0.032 STOM 0.825 CDC26 0.799 0.919 0.004 0.007 TDRD9 0.758 PLAC8 0.828 0.937 0.001 0.046 TDRD9 0.758 GIMAP7 0.804 0.891 0.015 0.05 TDRD9 0.758 LGALS1 0.722 0.898 0.003 0.018 TDRD9 0.758 HS2ST1_UBA2 0.668 0.836 0.022 0.03 TDRD9 0.758 NEK6_LOC1001290 0.671 0.821 0.041 0.049 TDRD9 0.758 GLT25D1 0.77 0.932 0.002 0.002 TDRD9 0.758 P4HA1_RPL17 0.664 0.845 0.045 0.003 TDRD9 0.758 C15orf54 0.72 0.859 0.037 0.002 TDRD9 0.758 KIAA0746 0.776 0.9 0.006 0.014 TDRD9 0.758 LAIR1_LAIR2 0.607 0.87 0.018 0 TDRD9 0.758 E2F6 0.787 0.882 0.039 0.003 TDRD9 0.758 CD151 0.718 0.9 0.014 0.001 TDRD9 0.758 FSD1L 0.674 0.847 0.04 0.003 TDRD9 0.758 C7orf58 0.831 0.957 0.002 0.002 TDRD9 0.758 CTSL1_CTSLL3 0.676 0.863 0.04 0.007 TDRD9 0.758 NP 0.726 0.863 0.048 0.03 TDRD9 0.758 IGLV6-57 0.761 0.864 0.016 0.033 KREMEN1 0.848 PLAC8 0.828 0.947 0.004 0.029 KREMEN1 0.848 CDS2 0.73 0.902 0.006 0.012 KREMEN1 0.848 SLC39A9 0.776 0.894 0.021 0.017 KREMEN1 0.848 LGALS2 0.922 0.987 0.002 0.012 KREMEN1 0.848 HIST1H3J 0.898 0.939 0.028 0.04 KREMEN1 0.848 CD300A 0.841 0.939 0.002 0.04 KREMEN1 0.848 MGST3 0.857 0.945 0.008 0.027 KREMEN1 0.848 IGK@_IGKC_IGKV 0.855 0.928 0.035 0.01 KREMEN1 0.848 IRF1 0.82 0.951 0.003 0.007 KREMEN1 0.848 C7orf58 0.831 0.96 0.014 0.001 KREMEN1 0.848 TNFRSF17 0.875 0.947 0.009 0.014 KREMEN1 0.848 CDC26 0.799 0.913 0.004 0.033 FAIM3 0.93 DNAJC13 0.819 0.989 0.04 0 FAIM3 0.93 GSR 0.799 0.973 0.045 0 FAIM3 0.93 SGMS2 0.938 0.99 0.036 0.012 FAIM3 0.93 METTL7B 0.898 0.954 0.05 0.023 FAIM3 0.93 C9orf72 0.796 0.976 0.038 0 FAIM3 0.93 SMPDL3A 0.797 0.992 0.019 0 FAIM3 0.93 NA 0.927 0.997 0.03 0.013 FAIM3 0.93 EIF2AK2 0.964 0.998 0.027 0.038 FAIM3 0.93 NA 0.868 0.992 0.02 0.001 FAIM3 0.93 CCDC125 0.917 1 0.025 0.006 FAIM3 0.93 MPZL3 0.892 0.966 0.017 0.018 FAIM3 0.93 SYNE2 0.748 0.994 0.038 0 FAIM3 0.93 RBP7 0.848 0.969 0.045 0.003 FAIM3 0.93 RGS2 0.915 0.975 0.02 0.03 FAIM3 0.93 PPP1R2_PPP1R2P3 0.702 0.993 0.041 0 ACER3 0.746 HLA-DPB1 0.815 0.934 0 0.009 ACER3 0.746 PLAC8 0.828 0.968 0 0.007 ACER3 0.746 SLC39A9 0.776 0.918 0 0.003 ACER3 0.746 GIMAP7 0.804 0.921 0.002 0.005 ACER3 0.746 LGALS1 0.722 0.923 0 0.002 ACER3 0.746 HLA-DRA 0.855 0.942 0.002 0.008 ACER3 0.746 SIAE 0.737 0.888 0.003 0.027 ACER3 0.746 AGTRAP 0.611 0.809 0.024 0.005 ACER3 0.746 MTRR 0.674 0.91 0.005 0 ACER3 0.746 AIG1 0.56 0.856 0.016 0 ACER3 0.746 HS2ST1_UBA2 0.668 0.838 0.016 0.018 ACER3 0.746 HIST1H3A 0.908 0.946 0 0.032 ACER3 0.746 HLA-DPA1 0.796 0.93 0 0.009 ACER3 0.746 SLC39A8 0.825 0.934 0.001 0.018 ACER3 0.746 PTGER2 0.881 0.959 0 0.015 ACER3 0.746 NEK6_LOC1001290 0.671 0.858 0.007 0.01 ACER3 0.746 TRAF3 0.87 0.953 0 0.048 ACER3 0.746 RPL17_SNORD58B 0.917 0.955 0 0.045 ACER3 0.746 JUP 0.753 0.888 0.009 0.035 ACER3 0.746 GALNT2 0.528 0.817 0.025 0.001 ACER3 0.746 HSP90AB1_HSP90A 0.924 0.984 0 0.029 ACER3 0.746 GLT25D1 0.77 0.952 0 0.003 ACER3 0.746 HDHD1A 0.602 0.855 0.024 0 ACER3 0.746 ITGA4_CERKL 0.88 0.956 0 0.022 ACER3 0.746 EIF1AX_SCARNA9L 0.871 0.944 0.001 0.015 ACER3 0.746 ICAM1 0.675 0.865 0.011 0.004 ACER3 0.746 P4HA1_RPL17 0.664 0.919 0.005 0 ACER3 0.746 C15orf54 0.72 0.844 0.041 0.002 ACER3 0.746 KLHL5 0.657 0.865 0.022 0.001 ACER3 0.746 MGST3 0.857 0.97 0 0.005 ACER3 0.746 KIAA0746 0.776 0.923 0.002 0.002 ACER3 0.746 HSPB1_HSPBL2 0.705 0.866 0.002 0.017 ACER3 0.746 FSD1L_GARNL1 0.779 0.902 0.001 0.015 ACER3 0.746 NA 0.7 0.886 0.042 0 ACER3 0.746 E2F6 0.787 0.863 0.05 0.002 ACER3 0.746 CD151 0.718 0.878 0.012 0.002 ACER3 0.746 SON 0.856 0.946 0 0.027 ACER3 0.746 C7orf58 0.831 0.97 0 0.001 ACER3 0.746 NP 0.726 0.915 0.003 0.001 ACER3 0.746 C4orf3 0.723 0.842 0.024 0.039 ACER3 0.746 TMEM62_SPCS2_L 0.78 0.903 0.024 0.001 ACER3 0.746 TAS2R31 0.833 0.925 0.002 0.002 ACER3 0.746 IGLV6-57 0.761 0.846 0.019 0.048 ACER3 0.746 SDHC 0.736 0.877 0.045 0 ACER3 0.746 NFXL1 0.728 0.865 0.021 0.002 ACER3 0.746 DCTN5 0.722 0.893 0.005 0.001 ACER3 0.746 KIAA0101_CSNK1G 0.736 0.851 0.027 0.002 TGFBR1 0.734 PTGDR 0.896 0.956 0.001 0.005 TGFBR1 0.734 SH2D1B 0.894 0.952 0 0.001 TGFBR1 0.734 LRRN3 0.928 0.95 0 0.043 TGFBR1 0.734 HLA-DPB1 0.815 0.888 0.005 0.024 TGFBR1 0.734 VAMP2 0.783 0.891 0.002 0.013 TGFBR1 0.734 CASS4 0.738 0.861 0.022 0.001 TGFBR1 0.734 KPNA5 0.895 0.935 0.002 0.016 TGFBR1 0.734 PLAC8 0.828 0.928 0.002 0.002 TGFBR1 0.734 KLRK1_KLRC4 0.865 0.908 0.015 0.023 TGFBR1 0.734 ITK 0.874 0.925 0.002 0.006 TGFBR1 0.734 FGFBP2 0.814 0.878 0.034 0.001 TGFBR1 0.734 CDS2 0.73 0.902 0.001 0.001 TGFBR1 0.734 SLC39A9 0.776 0.949 0 0 TGFBR1 0.734 GIMAP7 0.804 0.91 0.004 0 TGFBR1 0.734 ANAPC11 0.943 0.974 0 0.037 TGFBR1 0.734 INSIG1 0.957 0.992 0 0.048 TGFBR1 0.734 HIST1H4L 0.961 0.98 0 0.042 TGFBR1 0.734 LGALS1 0.722 0.848 0.022 0.008 TGFBR1 0.734 DNAJC9_FAM149B1 0.944 0.985 0 0.013 TGFBR1 0.734 SIAE 0.737 0.868 0.033 0.008 TGFBR1 0.734 HINT1 0.9 0.935 0.001 0.019 TGFBR1 0.734 HLA-DPA1 0.796 0.886 0.003 0.021 TGFBR1 0.734 VOPP1_LOC100128 0.938 0.985 0 0.029 TGFBR1 0.734 SLC39A8 0.825 0.899 0.014 0.008 TGFBR1 0.734 PTGER2 0.881 0.97 0 0.005 TGFBR1 0.734 PMS2CL_PMS2 0.942 0.984 0 0.02 TGFBR1 0.734 NEK6_LOC1001290 0.671 0.868 0.005 0.001 TGFBR1 0.734 CENPK 0.834 0.879 0.035 0.009 TGFBR1 0.734 TRAF3 0.87 0.922 0.002 0.035 TGFBR1 0.734 AMFR 0.867 0.959 0 0.011 TGFBR1 0.734 RPL17_SNORD58B 0.917 0.953 0 0.008 TGFBR1 0.734 HSP90AB1_HSP90A 0.924 0.971 0 0.011 TGFBR1 0.734 GLT25D1 0.77 0.873 0.006 0.026 TGFBR1 0.734 ITGA4_CERKL 0.88 0.966 0 0.003 TGFBR1 0.734 EIF1AX_SCARNA9L 0.871 0.937 0.001 0.002 TGFBR1 0.734 CD300A 0.841 0.935 0 0.013 TGFBR1 0.734 ZNF28 0.837 0.918 0.002 0.002 TGFBR1 0.734 MGST3 0.857 0.94 0 0.007 TGFBR1 0.734 CCR4 0.871 0.914 0.006 0.007 TGFBR1 0.734 FSD1L_GARNL1 0.779 0.918 0.001 0.001 TGFBR1 0.734 FAM118A 0.882 0.958 0 0.009 TGFBR1 0.734 S100B 0.841 0.89 0.022 0.011 TGFBR1 0.734 IGK@_IGKC_IGKV 0.855 0.906 0.003 0.009 TGFBR1 0.734 SON 0.856 0.953 0 0.003 TGFBR1 0.734 IRF1 0.82 0.941 0 0.005 TGFBR1 0.734 C7orf58 0.831 0.894 0.01 0.005 TGFBR1 0.734 B4GALT3 0.917 0.97 0 0.05 TGFBR1 0.734 NP 0.726 0.884 0.021 0 TGFBR1 0.734 TNFRSF17 0.875 0.924 0 0.022 TGFBR1 0.734 TMEM62_SPCS2_L 0.78 0.909 0.004 0 TGFBR1 0.734 CDC26 0.799 0.926 0 0.004 TGFBR1 0.734 TAS2R31 0.833 0.907 0.007 0 TGFBR1 0.734 DCTN5 0.722 0.851 0.046 0.002 GPR84 0.927 PLAC8 0.828 0.971 0.038 0.01 GPR84 0.927 HIST1H4E 0.915 0.989 0.045 0.033 GPR84 0.927 SLC39A8 0.825 0.971 0.039 0.012 GPR84 0.927 TRAF3 0.87 0.975 0.032 0.035 GPR84 0.927 RRP12_LOC644215 0.904 0.987 0.044 0.021 GPR84 0.927 IRF1 0.82 0.982 0.038 0.001 GPR84 0.927 CTSL1_CTSLL3 0.676 0.966 0.05 0 C7orf53 0.786 SH2D1B 0.894 0.943 0.002 0.009 C7orf53 0.786 HLA-DPB1 0.815 0.901 0.005 0.046 C7orf53 0.786 CASS4 0.738 0.898 0.004 0.006 C7orf53 0.786 PLAC8 0.828 0.93 0.002 0.022 C7orf53 0.786 CDS2 0.73 0.883 0 0.038 C7orf53 0.786 CHI3L1 0.722 0.902 0.006 0.002 C7orf53 0.786 SLC39A9 0.776 0.896 0.002 0.02 C7orf53 0.786 GIMAP7 0.804 0.905 0.015 0.004 C7orf53 0.786 LGALS1 0.722 0.852 0.015 0.034 C7orf53 0.786 SIAE 0.737 0.864 0.019 0.05 C7orf53 0.786 HS2ST1_UBA2 0.668 0.882 0.008 0.003 C7orf53 0.786 HLA-DPA1 0.796 0.895 0.006 0.038 C7orf53 0.786 NEK6_LOC1001290 0.671 0.863 0.029 0.004 C7orf53 0.786 GLT25D1 0.77 0.877 0.014 0.045 C7orf53 0.786 ITGA4_CERKL 0.88 0.94 0.001 0.043 C7orf53 0.786 ICAM1 0.675 0.855 0.04 0.004 C7orf53 0.786 KIAA0746 0.776 0.892 0.04 0.001 C7orf53 0.786 CCR4 0.871 0.932 0.002 0.031 C7orf53 0.786 FSD1L_GARNL1 0.779 0.868 0.025 0.05 C7orf53 0.786 E2F6 0.787 0.893 0.042 0.001 C7orf53 0.786 S100B 0.841 0.905 0.034 0.018 C7orf53 0.786 IGK@_IGKC_IGKV 0.855 0.92 0.003 0.034 C7orf53 0.786 C7orf58 0.831 0.971 0.001 0 C7orf53 0.786 NP 0.726 0.909 0.003 0.002 C7orf53 0.786 TAS2R31 0.833 0.904 0.009 0.017 PLB1 0.783 HLA-DPB1 0.815 0.938 0.002 0.006 PLB1 0.783 PLAC8 0.828 0.975 0 0.004 PLB1 0.783 CDS2 0.73 0.864 0.011 0.049 PLB1 0.783 CCR3 0.869 0.921 0.012 0.03 PLB1 0.783 SLC39A9 0.776 0.909 0.002 0.009 PLB1 0.783 GIMAP7 0.804 0.906 0.017 0.016 PLB1 0.783 LGALS1 0.722 0.906 0.003 0.004 PLB1 0.783 HLA-DRA 0.855 0.936 0.019 0.007 PLB1 0.783 SIAE 0.737 0.87 0.038 0.03 PLB1 0.783 MTRR 0.674 0.896 0.041 0 PLB1 0.783 HLA-DPA1 0.796 0.923 0.002 0.014 PLB1 0.783 PTGER2 0.881 0.951 0.001 0.027 PLB1 0.783 NEK6_LOC1001290 0.671 0.877 0.01 0.004 PLB1 0.783 TRAF3 0.87 0.954 0.002 0.024 PLB1 0.783 JUP 0.753 0.895 0.049 0.025 PLB1 0.783 GALNT2 0.528 0.849 0.004 0 PLB1 0.783 HSP90AB1_HSP90A 0.924 0.992 0 0.014 PLB1 0.783 GLT25D1 0.77 0.936 0.001 0.005 PLB1 0.783 EIF1AX_SCARNA9L 0.871 0.938 0.005 0.021 PLB1 0.783 MGST3 0.857 0.956 0 0.014 PLB1 0.783 KIAA0746 0.776 0.923 0.008 0.002 PLB1 0.783 HSPB1_HSPBL2 0.705 0.868 0.029 0.01 PLB1 0.783 CCR4 0.871 0.936 0.006 0.023 PLB1 0.783 FSD1L_GARNL1 0.779 0.877 0.015 0.044 PLB1 0.783 CD151 0.718 0.875 0.047 0.001 PLB1 0.783 IGK@_IGKC_IGKV 0.855 0.923 0.002 0.039 PLB1 0.783 C7orf58 0.831 0.963 0.002 0.001 PLB1 0.783 NP 0.726 0.901 0.016 0.001 PLB1 0.783 CDC26 0.799 0.894 0.004 0.035 PLB1 0.783 TAS2R31 0.833 0.919 0.014 0.003 PLB1 0.783 IGLV6-57 0.761 0.87 0.016 0.018 PLB1 0.783 NFXL1 0.728 0.879 0.048 0.001 PLB1 0.783 DCTN5 0.722 0.896 0.011 0.001 DSE 0.747 HLA-DPB1 0.815 0.915 0.001 0.023 DSE 0.747 KPNA5 0.895 0.933 0.002 0.049 DSE 0.747 PLAC8 0.828 0.978 0 0.003 DSE 0.747 ITK 0.874 0.934 0.001 0.011 DSE 0.747 FGFBP2 0.814 0.873 0.027 0.035 DSE 0.747 SLC39A9 0.776 0.899 0 0.019 DSE 0.747 GIMAP7 0.804 0.942 0.001 0.001 DSE 0.747 LGALS1 0.722 0.891 0 0.013 DSE 0.747 HLA-DRA 0.855 0.932 0.001 0.006 DSE 0.747 HINT1 0.9 0.944 0.001 0.039 DSE 0.747 MTRR 0.674 0.885 0.018 0 DSE 0.747 HS2ST1_UBA2 0.668 0.847 0.021 0.009 DSE 0.747 HLA-DPA1 0.796 0.919 0 0.013 DSE 0.747 SLC39A8 0.825 0.933 0.002 0.007 DSE 0.747 NEK6_LOC1001290 0.671 0.815 0.049 0.028 DSE 0.747 CENPK 0.834 0.886 0.029 0.01 DSE 0.747 TRAF3 0.87 0.966 0 0.017 DSE 0.747 RPL17_SNORD58B 0.917 0.95 0.001 0.038 DSE 0.747 JUP 0.753 0.877 0.017 0.045 DSE 0.747 HSP90AB1_HSP90A 0.924 0.994 0 0.015 DSE 0.747 GLT25D1 0.77 0.877 0.004 0.036 DSE 0.747 ITGA4_CERKL 0.88 0.944 0 0.032 DSE 0.747 EIF1AX_SCARNA9L 0.871 0.943 0 0.021 DSE 0.747 ICAM1 0.675 0.872 0.004 0.003 DSE 0.747 P4HA1_RPL17 0.664 0.861 0.05 0 DSE 0.747 KLHL5 0.657 0.896 0.014 0 DSE 0.747 MGST3 0.857 0.975 0 0.004 DSE 0.747 KIAA0746 0.776 0.928 0.002 0.001 DSE 0.747 HSPB1_HSPBL2 0.705 0.842 0.014 0.033 DSE 0.747 CCR4 0.871 0.941 0.001 0.019 DSE 0.747 LAIR1_LAIR2 0.607 0.874 0.016 0 DSE 0.747 CD151 0.718 0.852 0.048 0.005 DSE 0.747 IGK@_IGKC_IGKV 0.855 0.928 0 0.024 DSE 0.747 C7orf58 0.831 0.957 0 0.001 DSE 0.747 NP 0.726 0.893 0.016 0.001 DSE 0.747 TAS2R31 0.833 0.915 0.004 0.004 DSE 0.747 IGLV6-57 0.761 0.856 0.012 0.021 DSE 0.747 DCTN5 0.722 0.884 0.006 0.002 PTGDR 0.896 DNAJC13 0.819 0.955 0.011 0.004 PTGDR 0.896 GSR 0.799 0.942 0.035 0.002 PTGDR 0.896 PICALM 0.723 0.954 0.038 0 PTGDR 0.896 C9orf72 0.796 0.952 0.018 0.002 PTGDR 0.896 SMPDL3A 0.797 0.953 0.012 0.007 PTGDR 0.896 NA 0.868 0.952 0.023 0.025 PTGDR 0.896 DAAM2_LOC100131 0.903 0.96 0.034 0.044 PTGDR 0.896 IRS2 0.817 0.937 0.05 0.024 PTGDR 0.896 ACPL2 0.906 0.983 0.006 0.027 PTGDR 0.896 OCR1 0.896 0.987 0.013 0.01 PTGDR 0.896 NF-E4 0.861 0.961 0.044 0.006 PTGDR 0.896 KIAA1257_ACAD9/ 0.851 0.95 0.019 0.017 PTGDR 0.896 PDK4 0.863 0.966 0.028 0.005 PTGDR 0.896 ZRANB1 0.897 0.978 0.007 0.011 PTGDR 0.896 CYP4F3_CYP4F2 0.901 0.98 0.008 0.025 PTGDR 0.896 SYNE2 0.748 0.985 0.003 0 PTGDR 0.896 MME 0.894 0.979 0.01 0.017 PTGDR 0.896 RBP7 0.848 0.969 0.005 0.006 PTGDR 0.896 PPP1R2_PPP1R2P3 0.702 0.944 0.044 0 PTGDR 0.896 CALM2_C2orf61 0.749 0.962 0.026 0 CAMK4 0.921 TNFAIP6 0.911 0.989 0.05 0.021 CAMK4 0.921 ANKRD34B 0.904 0.969 0.033 0.044 CAMK4 0.921 C9orf72 0.796 0.96 0.026 0.002 CAMK4 0.921 SMPDL3A 0.797 0.985 0.038 0.001 CAMK4 0.921 NA 0.927 0.989 0.035 0.036 CAMK4 0.921 NA 0.868 0.97 0.033 0.011 CAMK4 0.921 GPR65 0.736 0.96 0.025 0 CAMK4 0.921 OCR1 0.896 0.987 0.036 0.021 CAMK4 0.921 CCDC125 0.917 0.991 0.032 0.015 CAMK4 0.921 ZRANB1 0.897 0.983 0.033 0.012 CAMK4 0.921 CYP4F3_CYP4F2 0.901 0.98 0.032 0.05 CAMK4 0.921 SYNE2 0.748 0.979 0.043 0 CAMK4 0.921 RBP7 0.848 0.959 0.045 0.016 CAMK4 0.921 PPP1R2_PPP1R2P3 0.702 0.987 0.032 0 DNAJC13 0.819 SH2D1B 0.894 0.978 0.001 0.002 DNAJC13 0.819 LRRN3 0.928 0.966 0.001 0.027 DNAJC13 0.819 HLA-DPB1 0.815 0.945 0.003 0.001 DNAJC13 0.819 VAMP2 0.783 0.9 0.012 0.019 DNAJC13 0.819 CASS4 0.738 0.902 0.015 0.002 DNAJC13 0.819 KPNA5 0.895 0.965 0.002 0.007 DNAJC13 0.819 PLAC8 0.828 0.977 0.001 0.001 DNAJC13 0.819 KLRK1_KLRC4 0.865 0.938 0.022 0.009 DNAJC13 0.819 ITK 0.874 0.962 0.003 0.008 DNAJC13 0.819 FGFBP2 0.814 0.918 0.038 0 DNAJC13 0.819 CDS2 0.73 0.909 0.001 0.005 DNAJC13 0.819 CCR3 0.869 0.935 0.007 0.029 DNAJC13 0.819 SEC24A_SAR1B 0.478 0.905 0.036 0 DNAJC13 0.819 SLC39A9 0.776 0.988 0 0 DNAJC13 0.819 GIMAP7 0.804 0.968 0.001 0 DNAJC13 0.819 LGALS1 0.722 0.885 0.032 0.006 DNAJC13 0.819 HLA-DRA 0.855 0.948 0.005 0.018 DNAJC13 0.819 DNAJC9_FAM149B1 0.944 0.987 0 0.016 DNAJC13 0.819 SIAE 0.737 0.92 0.016 0.003 DNAJC13 0.819 HINT1 0.9 0.947 0.007 0.013 DNAJC13 0.819 MTRR 0.674 0.957 0.004 0 DNAJC13 0.819 HS2ST1_UBA2 0.668 0.905 0.019 0 DNAJC13 0.819 HLA-DPA1 0.796 0.951 0.001 0.001 DNAJC13 0.819 SLC39A8 0.825 0.918 0.027 0.017 DNAJC13 0.819 PTGER2 0.881 0.977 0 0.003 DNAJC13 0.819 PMS2CL_PMS2 0.942 0.998 0 0.016 DNAJC13 0.819 NEK6_LOC1001290 0.671 0.903 0.008 0 DNAJC13 0.819 TRAF3 0.87 0.95 0.003 0.018 DNAJC13 0.819 RPL17_SNORD58B 0.917 0.965 0.003 0.007 DNAJC13 0.819 HSP90AB1_HSP90A 0.924 0.979 0 0.02 DNAJC13 0.819 GLT25D1 0.77 0.922 0.003 0.011 DNAJC13 0.819 ITGA4_CERKL 0.88 0.994 0 0.003 DNAJC13 0.819 EIF1AX_SCARNA9L 0.871 0.958 0.005 0.001 DNAJC13 0.819 ZNF28 0.837 0.933 0.008 0.004 DNAJC13 0.819 P4HA1_RPL17 0.664 0.952 0.007 0 DNAJC13 0.819 KLHL5 0.657 0.934 0.007 0 DNAJC13 0.819 MGST3 0.857 0.957 0 0.006 DNAJC13 0.819 KIAA0746 0.776 0.974 0.002 0 DNAJC13 0.819 CCR4 0.871 0.964 0.001 0.008 DNAJC13 0.819 FSD1L_GARNL1 0.779 0.93 0.003 0.003 DNAJC13 0.819 FAM118A 0.882 0.965 0.001 0.023 DNAJC13 0.819 IGK@_IGKC_IGKV 0.855 0.966 0.001 0.002 DNAJC13 0.819 SON 0.856 0.953 0.008 0.004 DNAJC13 0.819 IRF1 0.82 0.94 0 0.022 DNAJC13 0.819 C7orf58 0.831 0.965 0.002 0.001 DNAJC13 0.819 B4GALT3 0.917 0.98 0 0.044 DNAJC13 0.819 NP 0.726 0.939 0.01 0 DNAJC13 0.819 TNFRSF17 0.875 0.943 0.003 0.029 DNAJC13 0.819 TMEM62_SPCS2_L 0.78 0.946 0.024 0 DNAJC13 0.819 CDC26 0.799 0.926 0.001 0.009 DNAJC13 0.819 TAS2R31 0.833 0.942 0.011 0 DNAJC13 0.819 DCTN5 0.722 0.937 0.004 0 TNFAIP6 0.911 SH2D1B 0.894 0.988 0.026 0.002 TNFAIP6 0.911 LRRN3 0.928 0.992 0.019 0.019 TNFAIP6 0.911 HLA-DPB1 0.815 0.952 0.014 0.011 TNFAIP6 0.911 CASS4 0.738 0.953 0.019 0 TNFAIP6 0.911 KPNA5 0.895 0.982 0.044 0.008 TNFAIP6 0.911 PLAC8 0.828 0.969 0.012 0.01 TNFAIP6 0.911 ITK 0.874 0.978 0.025 0.013 TNFAIP6 0.911 SLC39A9 0.776 0.946 0.013 0.002 TNFAIP6 0.911 GIMAP7 0.804 0.978 0.034 0 TNFAIP6 0.911 LGALS2 0.922 0.985 0.006 0.031 TNFAIP6 0.911 HLA-DPA1 0.796 0.944 0.016 0.008 TNFAIP6 0.911 SLC39A8 0.825 0.969 0.007 0.012 TNFAIP6 0.911 TRAF3 0.87 0.965 0.01 0.042 TNFAIP6 0.911 JUP 0.753 0.943 0.047 0.008 TNFAIP6 0.911 HSP90AB1_HSP90A 0.924 0.991 0.012 0.026 TNFAIP6 0.911 EIF1AX_SCARNA9L 0.871 0.962 0.014 0.021 TNFAIP6 0.911 CD300A 0.841 0.952 0.006 0.024 TNFAIP6 0.911 ICAM1 0.675 0.946 0.01 0 TNFAIP6 0.911 MGST3 0.857 0.963 0.011 0.018 TNFAIP6 0.911 KIAA0746 0.776 0.966 0.033 0 TNFAIP6 0.911 IGK@_IGKC_IGKV 0.855 0.983 0.027 0.002 TNFAIP6 0.911 IRF1 0.82 0.963 0.003 0.009 TNFAIP6 0.911 C7orf58 0.831 0.981 0.044 0 TNFAIP6 0.911 TNFRSF17 0.875 0.975 0.022 0.009 TNFAIP6 0.911 CDC26 0.799 0.94 0.036 0.007 GSR 0.799 SH2D1B 0.894 0.938 0.005 0.014 GSR 0.799 HLA-DPB1 0.815 0.93 0.004 0.003 GSR 0.799 VAMP2 0.783 0.907 0.001 0.013 GSR 0.799 CASS4 0.738 0.897 0.015 0.001 GSR 0.799 KPNA5 0.895 0.928 0.007 0.037 GSR 0.799 PLAC8 0.828 0.962 0 0.002 GSR 0.799 KLRK1_KLRC4 0.865 0.907 0.027 0.041 GSR 0.799 ITK 0.874 0.937 0.008 0.012 GSR 0.799 CDS2 0.73 0.89 0.004 0.004 GSR 0.799 SLC39A9 0.776 0.988 0 0 GSR 0.799 GIMAP7 0.804 0.935 0.004 0.001 GSR 0.799 HLA-DRA 0.855 0.952 0.004 0.003 GSR 0.799 DNAJC9_FAM149B1 0.944 0.984 0 0.018 GSR 0.799 HLA-DPA1 0.796 0.925 0.002 0.003 GSR 0.799 PTGER2 0.881 0.952 0 0.025 GSR 0.799 PMS2CL_PMS2 0.942 0.985 0 0.033 GSR 0.799 NEK6_LOC1001290 0.671 0.88 0.037 0 GSR 0.799 TRAF3 0.87 0.966 0 0.007 GSR 0.799 RPL17_SNORD58B 0.917 0.942 0.005 0.047 GSR 0.799 HSP90AB1_HSP90A 0.924 0.994 0 0.006 GSR 0.799 GLT25D1 0.77 0.89 0.035 0.016 GSR 0.799 ITGA4_CERKL 0.88 0.955 0 0.013 GSR 0.799 EIF1AX_SCARNA9L 0.871 0.925 0.006 0.015 GSR 0.799 CD300A 0.841 0.927 0 0.032 GSR 0.799 ZNF28 0.837 0.893 0.024 0.032 GSR 0.799 MGST3 0.857 0.957 0.001 0.002 GSR 0.799 KIAA0746 0.776 0.915 0.031 0 GSR 0.799 CCR4 0.871 0.921 0.011 0.026 GSR 0.799 FSD1L_GARNL1 0.779 0.908 0.005 0.004 GSR 0.799 FAM118A 0.882 0.978 0 0.006 GSR 0.799 IGK@_IGKC_IGKV 0.855 0.94 0.005 0.003 GSR 0.799 SON 0.856 0.941 0.006 0.009 GSR 0.799 IRF1 0.82 0.947 0 0.005 GSR 0.799 C7orf58 0.831 0.914 0.031 0.002 GSR 0.799 TNFRSF17 0.875 0.951 0.002 0.005 GSR 0.799 CDC26 0.799 0.892 0.007 0.022 GSR 0.799 TAS2R31 0.833 0.905 0.031 0.002 GSR 0.799 DCTN5 0.722 0.92 0.008 0 KLRF1 0.8 PICALM 0.723 0.865 0.002 0.041 KLRF1 0.8 EAF2_HCG11_LOC 0.613 0.847 0.03 0.001 KLRF1 0.8 PDE3B 0.567 0.831 0.017 0.003 KLRF1 0.8 LRRFIP1 0.678 0.847 0.04 0.012 KLRF1 0.8 DPH3 0.749 0.885 0 0.039 KLRF1 0.8 NA 0.682 0.888 0.005 0.002 KLRF1 0.8 DLEU2_DLEU2L 0.711 0.898 0.001 0.005 KLRF1 0.8 ZNF587_ZNF417 0.632 0.848 0.014 0.003 KLRF1 0.8 CEP97 0.554 0.859 0.033 0 KLRF1 0.8 SYNE2 0.748 0.908 0 0.022 KLRF1 0.8 PLEKHF2 0.667 0.866 0.004 0.009 KLRF1 0.8 IL1RL1 0.738 0.879 0.026 0.013 KLRF1 0.8 MPZL2 0.699 0.838 0.033 0.042 KLRF1 0.8 PPP1R2_PPP1R2P3 0.702 0.861 0.003 0.028 KLRF1 0.8 ATP5L_ATP5L2 0.547 0.859 0.023 0 KLRF1 0.8 CALM2_C2orf61 0.749 0.885 0.001 0.043 SH2D1B 0.894 ANKRD34B 0.904 0.964 0.004 0.046 SH2D1B 0.894 SGMS2 0.938 0.989 0.003 0.027 SH2D1B 0.894 GK3P_GK 0.961 0.99 0.005 0.031 SH2D1B 0.894 PICALM 0.723 0.951 0.016 0 SH2D1B 0.894 C9orf72 0.796 0.955 0.003 0.002 SH2D1B 0.894 SMPDL3A 0.797 0.965 0.003 0.002 SH2D1B 0.894 TCN1 0.855 0.964 0.019 0.002 SH2D1B 0.894 HPGD 0.823 0.947 0.013 0.009 SH2D1B 0.894 FRMD3 0.755 0.944 0.006 0.001 SH2D1B 0.894 NA 0.927 0.998 0.002 0.013 SH2D1B 0.894 RUNX2 0.507 0.926 0.021 0 SH2D1B 0.894 TPST1 0.92 0.974 0.026 0.036 SH2D1B 0.894 EIF2AK2 0.964 1 0.002 0.032 SH2D1B 0.894 NA 0.868 0.962 0.002 0.019 SH2D1B 0.894 AREG 0.849 0.951 0.05 0.003 SH2D1B 0.894 DAAM2_LOC100131 0.903 0.967 0.014 0.021 SH2D1B 0.894 GPR65 0.736 0.947 0.009 0 SH2D1B 0.894 IRS2 0.817 0.938 0.044 0.011 SH2D1B 0.894 ACPL2 0.906 0.979 0.002 0.036 SH2D1B 0.894 OCR1 0.896 0.994 0.002 0.012 SH2D1B 0.894 DPH3 0.749 0.95 0.021 0 SH2D1B 0.894 NF-E4 0.861 0.974 0.003 0.008 SH2D1B 0.894 KIAA1257_ACAD9/ 0.851 0.965 0.003 0.005 SH2D1B 0.894 CCDC125 0.917 0.99 0.002 0.017 SH2D1B 0.894 PDK4 0.863 0.971 0.005 0.004 SH2D1B 0.894 THBS1 0.849 0.953 0.022 0.005 SH2D1B 0.894 ZRANB1 0.897 0.973 0.002 0.017 SH2D1B 0.894 ZNF587_ZNF417 0.632 0.942 0.021 0 SH2D1B 0.894 CYP4F3_CYP4F2 0.901 0.978 0.001 0.045 SH2D1B 0.894 SYNE2 0.748 0.981 0.002 0 SH2D1B 0.894 MME 0.894 0.994 0.002 0.009 SH2D1B 0.894 PLIN2 0.706 0.942 0.014 0 SH2D1B 0.894 FBXL13 0.795 0.971 0.006 0 SH2D1B 0.894 RBP7 0.848 0.963 0.002 0.01 SH2D1B 0.894 PPP1R2_PPP1R2P3 0.702 0.949 0.02 0 SH2D1B 0.894 HIST2H2BF_HIST2 0.855 0.938 0.032 0.041 SH2D1B 0.894 CALM2_C2orf61 0.749 0.966 0.016 0 SH2D1B 0.894 SPATA6 0.812 0.954 0.013 0.005 ANKRD34B 0.904 HLA-DPB1 0.815 0.953 0.047 0.001 ANKRD34B 0.904 PLAC8 0.828 0.962 0.034 0.003 ANKRD34B 0.904 HIST1H4E 0.915 0.971 0.007 0.05 ANKRD34B 0.904 CCR3 0.869 0.976 0.014 0.005 ANKRD34B 0.904 ANAPC11 0.943 0.984 0.004 0.027 ANKRD34B 0.904 HLA-DRA 0.855 0.965 0.013 0.015 ANKRD34B 0.904 DNAJC9_FAM149B1 0.944 0.985 0.006 0.03 ANKRD34B 0.904 LGALS2 0.922 0.977 0.004 0.018 ANKRD34B 0.904 SIAE 0.737 0.951 0.039 0 ANKRD34B 0.904 HIST1H3A 0.908 0.964 0.019 0.017 ANKRD34B 0.904 C22orf37 0.924 0.985 0.002 0.026 ANKRD34B 0.904 HLA-DPA1 0.796 0.957 0.041 0 ANKRD34B 0.904 PTGER2 0.881 0.971 0.01 0.005 ANKRD34B 0.904 PMS2CL_PMS2 0.942 0.988 0.002 0.032 ANKRD34B 0.904 NEK6_LOC1001290 0.671 0.949 0.038 0 ANKRD34B 0.904 IRF4 0.946 0.99 0.002 0.048 ANKRD34B 0.904 AMFR 0.867 0.965 0.003 0.021 ANKRD34B 0.904 HSP90AB1_HSP90A 0.924 0.971 0.018 0.021 ANKRD34B 0.904 GLT25D1 0.77 0.951 0.037 0.001 ANKRD34B 0.904 ITGA4_CERKL 0.88 0.98 0.01 0.004 ANKRD34B 0.904 CD300A 0.841 0.965 0.008 0.002 ANKRD34B 0.904 MGST3 0.857 0.955 0.025 0.002 ANKRD34B 0.904 CCR4 0.871 0.97 0.038 0.003 ANKRD34B 0.904 TYMS 0.863 0.969 0.007 0.006 ANKRD34B 0.904 RRP12_LOC644215 0.904 0.973 0.003 0.021 ANKRD34B 0.904 HIST1H2BM 0.891 0.971 0.015 0.009 ANKRD34B 0.904 ABCG1 0.85 0.964 0.007 0.007 ANKRD34B 0.904 HIST1H3B 0.89 0.973 0.015 0.008 ANKRD34B 0.904 FAM118A 0.882 0.969 0.011 0.016 ANKRD34B 0.904 APOLD1 0.871 0.973 0.006 0.006 ANKRD34B 0.904 IRF1 0.82 0.955 0.022 0.003 ANKRD34B 0.904 B4GALT3 0.917 0.981 0.003 0.043 ANKRD34B 0.904 CDC26 0.799 0.956 0.015 0 SGMS2 0.938 LRRN3 0.928 0.981 0.031 0.014 SGMS2 0.938 VAMP2 0.783 0.981 0.022 0 SGMS2 0.938 PLAC8 0.828 0.985 0.026 0.001 SGMS2 0.938 GOT2 0.966 0.994 0.019 0.042 SGMS2 0.938 SLC39A9 0.776 0.99 0.012 0 SGMS2 0.938 ANAPC11 0.943 0.984 0.025 0.02 SGMS2 0.938 INSIG1 0.957 0.995 0.009 0.046 SGMS2 0.938 HIST1H4L 0.961 0.988 0.038 0.047 SGMS2 0.938 HLA-DRA 0.855 0.99 0.011 0.004 SGMS2 0.938 DNAJC9_FAM149B1 0.944 0.992 0.022 0.021 SGMS2 0.938 LGALS2 0.922 0.98 0.02 0.015 SGMS2 0.938 C22orf37 0.924 0.973 0.038 0.03 SGMS2 0.938 HLA-DPA1 0.796 0.983 0.035 0 SGMS2 0.938 VOPP1_LOC100128 0.938 0.979 0.015 0.05 SGMS2 0.938 PTGER2 0.881 0.988 0.018 0.003 SGMS2 0.938 PMS2CL_PMS2 0.942 0.998 0.009 0.017 SGMS2 0.938 IRF4 0.946 0.994 0.01 0.035 SGMS2 0.938 GALNT2 0.528 0.971 0.034 0 SGMS2 0.938 HSP90AB1_HSP90A 0.924 0.998 0.01 0.009 SGMS2 0.938 GLT25D1 0.77 0.985 0.018 0 SGMS2 0.938 ITGA4_CERKL 0.88 0.991 0.019 0.002 SGMS2 0.938 MGST3 0.857 0.98 0.035 0.001 SGMS2 0.938 RRP12_LOC644215 0.904 0.979 0.027 0.016 SGMS2 0.938 ABCG1 0.85 0.981 0.026 0.003 SGMS2 0.938 TIMM10 0.949 0.987 0.021 0.029 SGMS2 0.938 FSD1L_GARNL1 0.779 0.986 0.018 0 SGMS2 0.938 IGK@_IGKC_IGKV 0.855 0.987 0.029 0.001 SGMS2 0.938 IRF1 0.82 0.974 0.046 0 SGMS2 0.938 B4GALT3 0.917 0.989 0.03 0.01 SGMS2 0.938 TNFRSF17 0.875 0.99 0.024 0.001 SGMS2 0.938 DCTN5 0.722 0.985 0.017 0 B3GNT5_MCF2L2 0.949 MRPL41 0.976 0.999 0.012 0.05 B3GNT5_MCF2L2 0.949 PLAC8 0.828 0.992 0.022 0.001 B3GNT5_MCF2L2 0.949 GOT2 0.966 0.989 0.033 0.044 B3GNT5_MCF2L2 0.949 HIST1H4E 0.915 0.998 0.01 0.015 B3GNT5_MCF2L2 0.949 CDS2 0.73 0.979 0.021 0 B3GNT5_MCF2L2 0.949 SLC39A9 0.776 0.982 0.041 0 B3GNT5_MCF2L2 0.949 ANAPC11 0.943 0.994 0.017 0.011 B3GNT5_MCF2L2 0.949 INSIG1 0.957 0.999 0.011 0.028 B3GNT5_MCF2L2 0.949 HIST1H4L 0.961 0.992 0.028 0.032 B3GNT5_MCF2L2 0.949 LGALS1 0.722 0.983 0.039 0 B3GNT5_MCF2L2 0.949 PPIF 0.934 0.998 0.01 0.05 B3GNT5_MCF2L2 0.949 DNAJC9_FAM149B1 0.944 0.997 0.013 0.027 B3GNT5_MCF2L2 0.949 LGALS2 0.922 0.995 0.02 0.004 B3GNT5_MCF2L2 0.949 HS2ST1_UBA2 0.668 0.979 0.049 0 B3GNT5_MCF2L2 0.949 C22orf37 0.924 0.993 0.019 0.011 B3GNT5_MCF2L2 0.949 VOPP1_LOC100128 0.938 0.991 0.011 0.026 B3GNT5_MCF2L2 0.949 SLC39A8 0.825 0.985 0.044 0.002 B3GNT5_MCF2L2 0.949 PTGER2 0.881 0.986 0.02 0.002 B3GNT5_MCF2L2 0.949 PMS2CL_PMS2 0.942 0.998 0.011 0.016 B3GNT5_MCF2L2 0.949 HSP90AB1_HSP90A 0.924 0.996 0.013 0.013 B3GNT5_MCF2L2 0.949 CD300A 0.841 0.987 0.019 0.001 B3GNT5_MCF2L2 0.949 ICAM1 0.675 0.981 0.043 0 B3GNT5_MCF2L2 0.949 MGST3 0.857 0.997 0.013 0.001 B3GNT5_MCF2L2 0.949 RRP12_LOC644215 0.904 0.984 0.024 0.014 B3GNT5_MCF2L2 0.949 TIMM10 0.949 0.995 0.011 0.027 B3GNT5_MCF2L2 0.949 FAM118A 0.882 0.987 0.024 0.009 B3GNT5_MCF2L2 0.949 APOLD1 0.871 0.984 0.034 0.002 B3GNT5_MCF2L2 0.949 IRF1 0.82 0.997 0.012 0 B3GNT5_MCF2L2 0.949 LASS4 0.924 0.982 0.019 0.038 B3GNT5_MCF2L2 0.949 B4GALT3 0.917 0.987 0.018 0.026 B3GNT5_MCF2L2 0.949 CDC26 0.799 0.984 0.02 0 GK3P_GK 0.961 HIST1H4E 0.915 0.984 0.028 0.047 GK3P_GK 0.961 DNAJC9_FAM149B1 0.944 0.997 0.03 0.03 GK3P_GK 0.961 LGALS2 0.922 0.99 0.032 0.013 GK3P_GK 0.961 HIST1H3A 0.908 0.989 0.033 0.006 GK3P_GK 0.961 C22orf37 0.924 0.982 0.03 0.046 GK3P_GK 0.961 PMS2CL_PMS2 0.942 0.993 0.025 0.036 GK3P_GK 0.961 AMFR 0.867 0.977 0.03 0.017 GK3P_GK 0.961 HSP90AB1_HSP90A 0.924 0.99 0.034 0.025 GK3P_GK 0.961 CD300A 0.841 0.982 0.031 0.001 GK3P_GK 0.961 MGST3 0.857 0.98 0.048 0.003 GK3P_GK 0.961 TYMS 0.863 0.976 0.043 0.004 GK3P_GK 0.961 RRP12_LOC644215 0.904 0.993 0.033 0.011 GK3P_GK 0.961 HIST1H3B 0.89 0.983 0.038 0.009 GK3P_GK 0.961 FAM118A 0.882 0.984 0.038 0.017 GK3P_GK 0.961 IRF1 0.82 0.985 0.023 0.001 PICALM 0.723 MRPL41 0.976 1 0 0.048 PICALM 0.723 CASS4 0.738 0.863 0.002 0.012 PICALM 0.723 PLAC8 0.828 0.907 0.001 0.015 PICALM 0.723 KLRK1_KLRC4 0.865 0.914 0.002 0.038 PICALM 0.723 ITK 0.874 0.917 0.001 0.022 PICALM 0.723 FGFBP2 0.814 0.89 0.006 0.004 PICALM 0.723 CDS2 0.73 0.857 0.001 0.037 PICALM 0.723 CHI3L1 0.722 0.861 0.013 0.005 PICALM 0.723 SLC39A9 0.776 0.917 0 0.002 PICALM 0.723 GIMAP7 0.804 0.906 0.001 0.004 PICALM 0.723 PHOSPHO1 0.721 0.821 0.039 0.025 PICALM 0.723 DNAJC9_FAM149B1 0.944 0.983 0 0.028 PICALM 0.723 HS2ST1_UBA2 0.668 0.851 0.016 0.003 PICALM 0.723 SLC39A8 0.825 0.886 0.01 0.026 PICALM 0.723 PTGER2 0.881 0.955 0 0.041 PICALM 0.723 NEK6_LOC1001290 0.671 0.814 0.043 0.019 PICALM 0.723 CENPK 0.834 0.898 0.007 0.009 PICALM 0.723 RPL17_SNORD58B 0.917 0.968 0 0.009 PICALM 0.723 EIF1AX_SCARNA9L 0.871 0.929 0 0.029 PICALM 0.723 CD300A 0.841 0.931 0 0.028 PICALM 0.723 ZNF28 0.837 0.915 0 0.026 PICALM 0.723 MGST3 0.857 0.937 0 0.012 PICALM 0.723 KIAA0746 0.776 0.85 0.04 0.006 PICALM 0.723 FSD1L_GARNL1 0.779 0.865 0.002 0.047 PICALM 0.723 E2F6 0.787 0.851 0.041 0.004 PICALM 0.723 S100B 0.841 0.893 0.012 0.007 PICALM 0.723 IRF1 0.82 0.923 0 0.032 PICALM 0.723 C7orf58 0.831 0.915 0.002 0.002 PICALM 0.723 NP 0.726 0.871 0.011 0.001 PICALM 0.723 C4orf3 0.723 0.827 0.036 0.025 PICALM 0.723 PLEKHA3 0.645 0.829 0.038 0.001 PICALM 0.723 TMEM62_SPCS2_L 0.78 0.868 0.017 0.002 PICALM 0.723 CDC26 0.799 0.938 0 0.007 PICALM 0.723 TAS2R31 0.833 0.916 0.001 0.002 PICALM 0.723 IGLV6-57 0.761 0.831 0.027 0.041 PICALM 0.723 NFXL1 0.728 0.858 0.031 0 PICALM 0.723 DCTN5 0.722 0.852 0.014 0.008 PICALM 0.723 KIAA0101_CSNK1G 0.736 0.841 0.037 0.003 METTL7B 0.898 HIST1H4E 0.915 0.973 0.02 0.01 METTL7B 0.898 HIST1H4L 0.961 0.99 0.006 0.017 METTL7B 0.898 PPIF 0.934 0.966 0.029 0.047 METTL7B 0.898 LGALS2 0.922 0.992 0.003 0.007 METTL7B 0.898 C22orf37 0.924 0.98 0.005 0.023 METTL7B 0.898 HSP90AB1_HSP90A 0.924 0.97 0.009 0.046 METTL7B 0.898 MGST3 0.857 0.957 0.028 0.005 METTL7B 0.898 TYMS 0.863 0.971 0.015 0.001 METTL7B 0.898 RRP12_LOC644215 0.904 0.965 0.016 0.031 METTL7B 0.898 TIMM10 0.949 0.983 0.004 0.048 METTL7B 0.898 FAM118A 0.882 0.956 0.036 0.025 METTL7B 0.898 LASS4 0.924 0.973 0.004 0.036 METTL7B 0.898 B4GALT3 0.917 0.979 0.008 0.02 C9orf72 0.796 LRRN3 0.928 0.965 0.001 0.012 C9orf72 0.796 HLA-DPB1 0.815 0.934 0.002 0.002 C9orf72 0.796 MRPL41 0.976 0.999 0 0.049 C9orf72 0.796 CASS4 0.738 0.915 0.004 0 C9orf72 0.796 KPNA5 0.895 0.951 0.005 0.003 C9orf72 0.796 PLAC8 0.828 0.969 0.001 0 C9orf72 0.796 KLRK1_KLRC4 0.865 0.92 0.032 0.009 C9orf72 0.796 ITK 0.874 0.943 0.007 0.003 C9orf72 0.796 CDS2 0.73 0.937 0 0.001 C9orf72 0.796 SLC39A9 0.776 0.969 0 0 C9orf72 0.796 GIMAP7 0.804 0.961 0.002 0 C9orf72 0.796 INSIG1 0.957 0.996 0 0.026 C9orf72 0.796 HIST1H4L 0.961 0.986 0 0.025 C9orf72 0.796 LGALS1 0.722 0.891 0.01 0.003 C9orf72 0.796 HLA-DRA 0.855 0.936 0.008 0.004 C9orf72 0.796 DNAJC9_FAM149B1 0.944 0.988 0 0.025 C9orf72 0.796 LGALS2 0.922 0.969 0 0.032 C9orf72 0.796 HINT1 0.9 0.955 0.002 0.013 C9orf72 0.796 MTRR 0.674 0.911 0.05 0 C9orf72 0.796 HS2ST1_UBA2 0.668 0.914 0.006 0 C9orf72 0.796 HLA-DPA1 0.796 0.942 0.001 0.001 C9orf72 0.796 SLC39A8 0.825 0.923 0.03 0.002 C9orf72 0.796 PTGER2 0.881 0.967 0 0.017 C9orf72 0.796 PMS2CL_PMS2 0.942 0.988 0 0.014 C9orf72 0.796 NEK6_LOC1001290 0.671 0.887 0.016 0 C9orf72 0.796 TRAF3 0.87 0.967 0 0.008 C9orf72 0.796 RPL17_SNORD58B 0.917 0.962 0.001 0.015 C9orf72 0.796 HSP90AB1_HSP90A 0.924 0.977 0 0.011 C9orf72 0.796 GLT25D1 0.77 0.894 0.013 0.01 C9orf72 0.796 ITGA4_CERKL 0.88 0.984 0 0.003 C9orf72 0.796 EIF1AX_SCARNA9L 0.871 0.954 0.002 0.002 C9orf72 0.796 CD300A 0.841 0.934 0 0.038 C9orf72 0.796 ZNF28 0.837 0.922 0.007 0.01 C9orf72 0.796 P4HA1_RPL17 0.664 0.924 0.02 0 C9orf72 0.796 KLHL5 0.657 0.916 0.04 0 C9orf72 0.796 MGST3 0.857 0.979 0 0 C9orf72 0.796 KIAA0746 0.776 0.915 0.04 0 C9orf72 0.796 CCR4 0.871 0.951 0.004 0.001 C9orf72 0.796 FSD1L_GARNL1 0.779 0.9 0.011 0.003 C9orf72 0.796 FAM118A 0.882 0.957 0 0.042 C9orf72 0.796 IGK@_IGKC_IGKV 0.855 0.935 0.006 0.003 C9orf72 0.796 SON 0.856 0.938 0.002 0.021 C9orf72 0.796 IRF1 0.82 0.941 0 0.012 C9orf72 0.796 C7orf58 0.831 0.96 0.002 0 C9orf72 0.796 NP 0.726 0.925 0.012 0 C9orf72 0.796 TNFRSF17 0.875 0.931 0.003 0.029 C9orf72 0.796 CDC26 0.799 0.921 0 0.008 C9orf72 0.796 TAS2R31 0.833 0.923 0.012 0.001 C9orf72 0.796 DCTN5 0.722 0.927 0.008 0 SLC15A2 0.534 C7orf58 0.831 0.932 0 0.004 TLR10 0.521 C7orf58 0.831 0.9 0 0.029 LRRN3 0.928 SMPDL3A 0.797 0.971 0.044 0.001 LRRN3 0.928 NA 0.927 0.989 0.007 0.035 LRRN3 0.928 NA 0.868 0.976 0.008 0.005 LRRN3 0.928 GPR65 0.736 0.961 0.022 0 LRRN3 0.928 ACPL2 0.906 0.972 0.043 0.031 LRRN3 0.928 OCR1 0.896 0.981 0.013 0.028 LRRN3 0.928 CCDC125 0.917 0.982 0.014 0.028 LRRN3 0.928 ZRANB1 0.897 0.98 0.009 0.011 LRRN3 0.928 CYP4F3_CYP4F2 0.901 0.98 0.006 0.047 LRRN3 0.928 MME 0.894 0.98 0.031 0.015 LRRN3 0.928 PLIN2 0.706 0.956 0.038 0 HLA-DPB1 0.815 SMPDL3A 0.797 0.941 0.002 0.004 HLA-DPB1 0.815 TCN1 0.855 0.915 0.02 0.02 HLA-DPB1 0.815 HPGD 0.823 0.947 0.002 0.011 HLA-DPB1 0.815 FRMD3 0.755 0.881 0.021 0.017 HLA-DPB1 0.815 NA 0.927 0.97 0.001 0.041 HLA-DPB1 0.815 NA 0.868 0.957 0 0.014 HLA-DPB1 0.815 OR9A2 0.823 0.894 0.038 0.027 HLA-DPB1 0.815 PDK4 0.863 0.974 0.001 0.001 HLA-DPB1 0.815 SYNE2 0.748 0.898 0.037 0.001 HLA-DPB1 0.815 PLIN2 0.706 0.908 0.018 0.001 HLA-DPB1 0.815 HIST2H2BF_HIST2 0.855 0.913 0.027 0.048 VAMP2 0.783 TCN1 0.855 0.921 0.017 0.004 VAMP2 0.783 LOC284757 0.939 0.978 0 0.02 VAMP2 0.783 HPGD 0.823 0.905 0.02 0.029 VAMP2 0.783 HSPC159 0.809 0.883 0.039 0.018 VAMP2 0.783 FRMD3 0.755 0.903 0.006 0.002 VAMP2 0.783 NA 0.927 0.951 0.001 0.045 VAMP2 0.783 TPST1 0.92 0.955 0.003 0.037 VAMP2 0.783 EIF2AK2 0.964 0.994 0 0.049 VAMP2 0.783 NA 0.868 0.92 0.003 0.021 VAMP2 0.783 CDA 0.945 0.98 0 0.039 VAMP2 0.783 SAP30 0.911 0.961 0 0.029 VAMP2 0.783 AREG 0.849 0.903 0.01 0.037 VAMP2 0.783 DAAM2_LOC100131 0.903 0.956 0.001 0.021 VAMP2 0.783 IRS2 0.817 0.932 0.002 0.006 VAMP2 0.783 ACPL2 0.906 0.981 0 0.013 VAMP2 0.783 OCR1 0.896 0.935 0.006 0.023 VAMP2 0.783 ERGIC1 0.886 0.965 0.001 0.013 VAMP2 0.783 NF-E4 0.861 0.928 0.014 0.002 VAMP2 0.783 HAL 0.942 0.984 0 0.021 VAMP2 0.783 KIAA1257_ACAD9/ 0.851 0.93 0.007 0.002 VAMP2 0.783 CCDC125 0.917 0.98 0 0.009 VAMP2 0.783 PDK4 0.863 0.903 0.017 0.03 VAMP2 0.783 THBS1 0.849 0.923 0.009 0.007 VAMP2 0.783 ZRANB1 0.897 0.949 0 0.023 VAMP2 0.783 MPZL3 0.892 0.939 0 0.042 VAMP2 0.783 CYP4F3_CYP4F2 0.901 0.955 0.003 0.014 VAMP2 0.783 SYNE2 0.748 0.896 0.037 0 VAMP2 0.783 MME 0.894 0.945 0.004 0.006 VAMP2 0.783 CPM 0.854 0.901 0.029 0.036 VAMP2 0.783 RBP7 0.848 0.946 0.001 0.005 VAMP2 0.783 HIST2H2BF_HIST2 0.855 0.902 0.047 0.004 VAMP2 0.783 SPATA6 0.812 0.901 0.027 0.018 SMPDL3A 0.797 CASS4 0.738 0.884 0.042 0.001 SMPDL3A 0.797 KPNA5 0.895 0.966 0.004 0.007 SMPDL3A 0.797 PLAC8 0.828 0.985 0 0.002 SMPDL3A 0.797 KLRK1_KLRC4 0.865 0.937 0.03 0.003 SMPDL3A 0.797 ITK 0.874 0.968 0.003 0.006 SMPDL3A 0.797 FGFBP2 0.814 0.925 0.034 0 SMPDL3A 0.797 CCR3 0.869 0.92 0.011 0.023 SMPDL3A 0.797 SLC39A9 0.776 0.935 0.002 0.001 SMPDL3A 0.797 GIMAP7 0.804 0.99 0 0 SMPDL3A 0.797 LGALS1 0.722 0.921 0.002 0.001 SMPDL3A 0.797 HLA-DRA 0.855 0.924 0.017 0.009 SMPDL3A 0.797 DNAJC9_FAM149B1 0.944 0.991 0 0.03 SMPDL3A 0.797 LGALS2 0.922 0.973 0 0.042 SMPDL3A 0.797 HINT1 0.9 0.975 0.002 0.009 SMPDL3A 0.797 MTRR 0.674 0.948 0.006 0 SMPDL3A 0.797 HS2ST1_UBA2 0.668 0.897 0.046 0 SMPDL3A 0.797 HIST1H3A 0.908 0.941 0.004 0.047 SMPDL3A 0.797 HLA-DPA1 0.796 0.929 0.004 0.003 SMPDL3A 0.797 SLC39A8 0.825 0.985 0 0.003 SMPDL3A 0.797 PTGER2 0.881 0.964 0.001 0.012 SMPDL3A 0.797 PMS2CL_PMS2 0.942 0.994 0 0.017 SMPDL3A 0.797 TRAF3 0.87 0.976 0.001 0.011 SMPDL3A 0.797 RPL17_SNORD58B 0.917 0.962 0.004 0.008 SMPDL3A 0.797 HSP90AB1_HSP90A 0.924 0.996 0 0.012 SMPDL3A 0.797 GLT25D1 0.77 0.893 0.03 0.009 SMPDL3A 0.797 ITGA4_CERKL 0.88 0.978 0.001 0.005 SMPDL3A 0.797 EIF1AX_SCARNA9L 0.871 0.949 0.004 0.007 SMPDL3A 0.797 ZNF28 0.837 0.909 0.023 0.018 SMPDL3A 0.797 ICAM1 0.675 0.877 0.007 0.004 SMPDL3A 0.797 KLHL5 0.657 0.923 0.027 0 SMPDL3A 0.797 MGST3 0.857 0.982 0 0.001 SMPDL3A 0.797 KIAA0746 0.776 0.968 0.002 0 SMPDL3A 0.797 HSPB1_HSPBL2 0.705 0.881 0.006 0.007 SMPDL3A 0.797 CCR4 0.871 0.974 0.001 0.007 SMPDL3A 0.797 FAM118A 0.882 0.969 0.001 0.019 SMPDL3A 0.797 CD151 0.718 0.893 0.039 0 SMPDL3A 0.797 HIST1H3H 0.861 0.897 0.044 0.046 SMPDL3A 0.797 IGK@_IGKC_IGKV 0.855 0.954 0.006 0.002 SMPDL3A 0.797 SON 0.856 0.93 0.01 0.022 SMPDL3A 0.797 IRF1 0.82 0.93 0.001 0.012 SMPDL3A 0.797 C7orf58 0.831 0.933 0.01 0.008 SMPDL3A 0.797 NP 0.726 0.921 0.032 0 SMPDL3A 0.797 TNFRSF17 0.875 0.932 0.009 0.043 SMPDL3A 0.797 CDC26 0.799 0.901 0.006 0.021 SMPDL3A 0.797 TAS2R31 0.833 0.94 0.01 0 SMPDL3A 0.797 DCTN5 0.722 0.911 0.019 0 JKAMP 0.493 RPL17_SNORD58B 0.917 0.947 0 0.024 MRPL41 0.976 PPP2R5A_SNORA16 0.953 0.999 0.05 0.02 MRPL41 0.976 MTHFS 0.933 0.999 0.048 0.019 MRPL41 0.976 EIF2AK2 0.964 0.999 0.049 0.035 MRPL41 0.976 ACPL2 0.906 0.999 0.049 0.007 MRPL41 0.976 CCDC125 0.917 0.996 0.05 0.008 MRPL41 0.976 RBP7 0.848 1 0.048 0 OLFM4 0.715 LTF 0.593 0.776 0.025 0.003 OLFM4 0.715 DEFA4_DEFA8P 0.481 0.81 0.048 0 OLFM4 0.715 MPO 0.508 0.775 0.008 0 CASS4 0.738 LOC284757 0.939 0.971 0 0.033 CASS4 0.738 HSPC159 0.809 0.879 0.022 0.01 CASS4 0.738 NA 0.927 0.987 0 0.005 CASS4 0.738 PRR13_PCBP2 0.947 0.987 0 0.041 CASS4 0.738 NA 0.868 0.93 0 0.048 CASS4 0.738 GPR65 0.736 0.858 0.016 0.008 CASS4 0.738 OR9A2 0.823 0.877 0.005 0.043 CASS4 0.738 OCR1 0.896 0.951 0 0.006 CASS4 0.738 NF-E4 0.861 0.942 0.001 0.001 CASS4 0.738 KIAA1257_ACAD9/ 0.851 0.983 0 0 CASS4 0.738 CCDC125 0.917 0.974 0 0.025 CASS4 0.738 PDK4 0.863 0.909 0.002 0.04 CASS4 0.738 THBS1 0.849 0.92 0.001 0.007 CASS4 0.738 ZRANB1 0.897 0.977 0 0.002 CASS4 0.738 ZNF587_ZNF417 0.632 0.883 0.018 0 CASS4 0.738 CYP4F3_CYP4F2 0.901 0.958 0 0.026 CASS4 0.738 SYNE2 0.748 0.889 0.01 0 CASS4 0.738 MME 0.894 0.951 0 0.013 CASS4 0.738 FBXL13 0.795 0.89 0.012 0.003 CASS4 0.738 HIST2H2BF_HIST2 0.855 0.924 0.001 0.014 CASS4 0.738 SPATA6 0.812 0.891 0.003 0.045 TCN1 0.855 KPNA5 0.895 0.965 0.003 0.033 TCN1 0.855 KLRK1_KLRC4 0.865 0.949 0.028 0.011 TCN1 0.855 ITK 0.874 0.952 0.008 0.034 TCN1 0.855 FGFBP2 0.814 0.932 0.032 0.004 TCN1 0.855 CCR3 0.869 0.963 0.001 0.025 TCN1 0.855 SLC39A9 0.776 0.906 0.002 0.02 TCN1 0.855 GIMAP7 0.804 0.923 0.004 0.015 TCN1 0.855 LGALS1 0.722 0.889 0.024 0.007 TCN1 0.855 PHOSPHO1 0.721 0.948 0.002 0.001 TCN1 0.855 DNAJC9_FAM149B1 0.944 0.994 0 0.042 TCN1 0.855 HINT1 0.9 0.969 0.002 0.04 TCN1 0.855 HIST1H3J 0.898 0.973 0.003 0.007 TCN1 0.855 HIST1H3A 0.908 0.972 0.001 0.03 TCN1 0.855 HLA-DPA1 0.796 0.893 0.025 0.037 TCN1 0.855 SLC39A8 0.825 0.938 0.001 0.049 TCN1 0.855 MKI67 0.621 0.914 0.013 0 TCN1 0.855 NEK6_LOC1001290 0.671 0.887 0.033 0.001 TCN1 0.855 CENPK 0.834 0.946 0.031 0.002 TCN1 0.855 RPL17_SNORD58B 0.917 0.973 0.001 0.045 TCN1 0.855 EIF1AX_SCARNA9L 0.871 0.961 0.002 0.015 TCN1 0.855 ZNF28 0.837 0.948 0.002 0.014 TCN1 0.855 KIAA0746 0.776 0.912 0.03 0.007 TCN1 0.855 CCR4 0.871 0.945 0.005 0.049 TCN1 0.855 HIST1H3B 0.89 0.977 0.001 0.015 TCN1 0.855 FSD1L_GARNL1 0.779 0.929 0.001 0.01 TCN1 0.855 HIST1H3C 0.863 0.956 0.015 0.001 TCN1 0.855 HIST1H3H 0.861 0.955 0.006 0.005 TCN1 0.855 TPX2 0.635 0.901 0.022 0 TCN1 0.855 IGK@_IGKC_IGKV 0.855 0.925 0.036 0.024 TCN1 0.855 CLC 0.782 0.958 0.006 0.002 TCN1 0.855 SON 0.856 0.953 0.001 0.041 TCN1 0.855 C7orf58 0.831 0.906 0.049 0.026 TCN1 0.855 MPO 0.508 0.912 0.024 0 TCN1 0.855 NP 0.726 0.921 0.004 0.002 TCN1 0.855 C4orf3 0.723 0.889 0.045 0.008 TCN1 0.855 CDC26 0.799 0.922 0.003 0.022 TCN1 0.855 TAS2R31 0.833 0.951 0.007 0.003 TCN1 0.855 IGLV6-57 0.761 0.909 0.046 0.003 CLU 0.664 ITGA2B 0.58 0.806 0.039 0.001 CLU 0.664 PTGS1 0.675 0.89 0 0.002 CLU 0.664 E2F6 0.787 0.842 0.01 0.042 CLU 0.664 KIAA0101_CSNK1G 0.736 0.82 0.008 0.024 KPNA5 0.895 HPGD 0.823 0.95 0.024 0.016 KPNA5 0.895 NA 0.927 0.99 0.008 0.023 KPNA5 0.895 NA 0.868 0.944 0.023 0.044 KPNA5 0.895 GPR65 0.736 0.962 0.003 0 KPNA5 0.895 OCR1 0.896 0.979 0.009 0.032 KPNA5 0.895 DPH3 0.749 0.948 0.014 0 KPNA5 0.895 NF-E4 0.861 0.957 0.042 0.014 KPNA5 0.895 NA 0.682 0.956 0.041 0 KPNA5 0.895 KIAA1257_ACAD9/ 0.851 0.945 0.026 0.02 KPNA5 0.895 CCDC125 0.917 0.98 0.005 0.042 KPNA5 0.895 PDK4 0.863 0.961 0.02 0.007 KPNA5 0.895 ZRANB1 0.897 0.984 0.007 0.008 KPNA5 0.895 SYNE2 0.748 0.97 0.006 0 KPNA5 0.895 MME 0.894 0.983 0.014 0.011 KPNA5 0.895 RBP7 0.848 0.955 0.007 0.024 KPNA5 0.895 PPP1R2_PPP1R2P3 0.702 0.957 0.016 0 PLAC8 0.828 HPGD 0.823 0.946 0.007 0.008 PLAC8 0.828 HSPC159 0.809 0.917 0.048 0.003 PLAC8 0.828 TMTC1 0.809 0.942 0.027 0.001 PLAC8 0.828 SH3PXD2B 0.9 0.948 0.03 0.032 PLAC8 0.828 FRMD3 0.755 0.891 0.04 0.016 PLAC8 0.828 NA 0.927 0.989 0.002 0.012 PLAC8 0.828 MTHFS 0.933 0.995 0.001 0.031 PLAC8 0.828 EIF2AK2 0.964 0.997 0.001 0.035 PLAC8 0.828 NA 0.868 0.952 0.007 0.012 PLAC8 0.828 GPR65 0.736 0.953 0.002 0 PLAC8 0.828 OR9A2 0.823 0.916 0.01 0.018 PLAC8 0.828 PDK4 0.863 0.983 0.003 0.001 PLAC8 0.828 PLIN2 0.706 0.926 0.014 0 PLAC8 0.828 PPP1R2_PPP1R2P3 0.702 0.916 0.025 0 CD63 0.612 PDK4 0.863 0.903 0 0.03 HPSE 0.462 C7orf58 0.831 0.928 0 0.017 C1orf161 0.842 CDS2 0.73 0.903 0.037 0.006 C1orf161 0.842 HIST1H3A 0.908 0.961 0.022 0.007 C1orf161 0.842 CD300A 0.841 0.942 0.01 0.016 C1orf161 0.842 HIST1H2BM 0.891 0.956 0.013 0.041 C1orf161 0.842 HIST1H3B 0.89 0.956 0.02 0.017 C1orf161 0.842 APOLD1 0.871 0.966 0.008 0.008 C1orf161 0.842 CDC26 0.799 0.908 0.04 0.037 DDAH2 0.681 LGALS1 0.722 0.82 0.049 0.025 DDAH2 0.681 C22orf37 0.924 0.957 0 0.048 DDAH2 0.681 GLT25D1 0.77 0.878 0.008 0.007 DDAH2 0.681 CD300A 0.841 0.902 0 0.043 DDAH2 0.681 MGST3 0.857 0.926 0 0.029 DDAH2 0.681 S100B 0.841 0.864 0.019 0.032 DDAH2 0.681 IRF1 0.82 0.903 0.001 0.006 KLRK1_KLRC4 0.865 AREG 0.849 0.951 0.018 0.014 KLRK1_KLRC4 0.865 GPR65 0.736 0.932 0.006 0.003 KLRK1_KLRC4 0.865 OCR1 0.896 0.971 0.01 0.036 KLRK1_KLRC4 0.865 DPH3 0.749 0.929 0.02 0.001 KLRK1_KLRC4 0.865 NF-E4 0.861 0.945 0.023 0.038 KLRK1_KLRC4 0.865 PDK4 0.863 0.942 0.01 0.048 KLRK1_KLRC4 0.865 ZRANB1 0.897 0.968 0.003 0.043 KLRK1_KLRC4 0.865 SYNE2 0.748 0.962 0.002 0.001 KLRK1_KLRC4 0.865 MME 0.894 0.973 0.005 0.042 KLRK1_KLRC4 0.865 PPP1R2_PPP1R2P3 0.702 0.931 0.02 0 KLRK1_KLRC4 0.865 CALM2_C2orf61 0.749 0.935 0.018 0.002 ATP13A3 0.545 MTRR 0.674 0.814 0 0.032 ATP13A3 0.545 KIAA0746 0.776 0.896 0 0.007 ATP13A3 0.545 C7orf58 0.831 0.906 0 0.04 ITK 0.874 HPGD 0.823 0.936 0.018 0.04 ITK 0.874 FRMD3 0.755 0.913 0.045 0.009 ITK 0.874 NA 0.927 0.987 0.005 0.036 ITK 0.874 NA 0.868 0.966 0.002 0.02 ITK 0.874 GPR65 0.736 0.948 0.004 0.001 ITK 0.874 LRRFIP1 0.678 0.944 0.021 0 ITK 0.874 OCR1 0.896 0.977 0.008 0.025 ITK 0.874 KIAA1257_ACAD9/ 0.851 0.933 0.04 0.043 ITK 0.874 CCDC125 0.917 0.99 0.004 0.018 ITK 0.874 PDK4 0.863 0.955 0.011 0.019 ITK 0.874 ZRANB1 0.897 0.978 0.005 0.016 ITK 0.874 ZNF587_ZNF417 0.632 0.941 0.03 0 ITK 0.874 CYP4F3_CYP4F2 0.901 0.976 0.005 0.05 ITK 0.874 SYNE2 0.748 0.973 0.005 0 ITK 0.874 MME 0.894 0.974 0.014 0.017 ITK 0.874 PLIN2 0.706 0.936 0.01 0.001 ITK 0.874 RBP7 0.848 0.935 0.013 0.046 ITK 0.874 PPP1R2_PPP1R2P3 0.702 0.975 0.005 0 LOC284757 0.939 SLC39A9 0.776 0.975 0.026 0 LOC284757 0.939 HLA-DRA 0.855 0.979 0.04 0.011 LOC284757 0.939 DNAJC9_FAM149B1 0.944 0.988 0.047 0.035 LOC284757 0.939 PMS2CL_PMS2 0.942 0.988 0.033 0.044 LOC284757 0.939 TRAF3 0.87 0.975 0.027 0.022 LOC284757 0.939 MGST3 0.857 0.967 0.05 0.009 LOC284757 0.939 ABCG1 0.85 0.986 0.018 0.005 GOT2 0.966 PRR13_PCBP2 0.947 0.988 0.044 0.049 GOT2 0.966 HAL 0.942 0.988 0.033 0.025 GOT2 0.966 MPZL3 0.892 0.979 0.04 0.01 B3GAT3 0.973 PPP2R5A_SNORA16 0.953 1 0.046 0.019 B3GAT3 0.973 MTHFS 0.933 0.999 0.046 0.018 B3GAT3 0.973 CDA 0.945 1 0.046 0.022 HIST1H4E 0.915 PPP2R5A_SNORA16 0.953 0.993 0.022 0.018 HIST1H4E 0.915 PRR13_PCBP2 0.947 0.986 0.023 0.025 HIST1H4E 0.915 MTHFS 0.933 0.995 0.018 0.016 HIST1H4E 0.915 CDA 0.945 0.994 0.014 0.021 HIST1H4E 0.915 SAP30 0.911 0.971 0.037 0.039 HIST1H4E 0.915 HAL 0.942 0.978 0.035 0.034 HIST1H4E 0.915 MPZL3 0.892 0.986 0.012 0.005 HIST1H4E 0.915 RBP7 0.848 0.981 0.046 0 HIST1H4E 0.915 RGS2 0.915 0.987 0.013 0.011 HPGD 0.823 CDS2 0.73 0.888 0.039 0.006 HPGD 0.823 CCR3 0.869 0.941 0.024 0.004 HPGD 0.823 SLC39A9 0.776 0.915 0.015 0.004 HPGD 0.823 GIMAP7 0.804 0.918 0.049 0.002 HPGD 0.823 LGALS2 0.922 0.973 0.001 0.036 HPGD 0.823 SIAE 0.737 0.92 0.034 0.003 HPGD 0.823 HINT1 0.9 0.947 0.015 0.041 HPGD 0.823 HIST1H3J 0.898 0.936 0.009 0.037 HPGD 0.823 HIST1H3A 0.908 0.95 0.007 0.018 HPGD 0.823 HLA-DPA1 0.796 0.955 0.005 0 HPGD 0.823 PTGER2 0.881 0.973 0.001 0.02 HPGD 0.823 PMS2CL_PMS2 0.942 0.987 0.001 0.019 HPGD 0.823 TRAF3 0.87 0.935 0.012 0.048 HPGD 0.823 RPL17_SNORD58B 0.917 0.965 0.005 0.034 HPGD 0.823 GLT25D1 0.77 0.928 0.011 0.008 HPGD 0.823 ITGA4_CERKL 0.88 0.987 0.002 0.002 HPGD 0.823 EIF1AX_SCARNA9L 0.871 0.944 0.014 0.002 HPGD 0.823 ZNF28 0.837 0.924 0.029 0.01 HPGD 0.823 MGST3 0.857 0.933 0.007 0.032 HPGD 0.823 CCR4 0.871 0.944 0.026 0.004 HPGD 0.823 FSD1L_GARNL1 0.779 0.909 0.028 0.003 HPGD 0.823 HIST1H3H 0.861 0.923 0.031 0.004 HPGD 0.823 S100B 0.841 0.923 0.018 0.006 HPGD 0.823 IGK@_IGKC_IGKV 0.855 0.932 0.03 0.01 HPGD 0.823 SON 0.856 0.925 0.019 0.018 HPGD 0.823 C7orf58 0.831 0.95 0.012 0.003 HPGD 0.823 CDC26 0.799 0.915 0.03 0.004 HPGD 0.823 TAS2R31 0.833 0.922 0.043 0.002 FGFBP2 0.814 TMTC1 0.809 0.894 0.035 0.035 FGFBP2 0.814 AREG 0.849 0.92 0.005 0.037 FGFBP2 0.814 GPR65 0.736 0.884 0.008 0.015 FGFBP2 0.814 DPH3 0.749 0.897 0.007 0.005 FGFBP2 0.814 NF-E4 0.861 0.939 0.002 0.031 FGFBP2 0.814 SYNE2 0.748 0.947 0 0 FGFBP2 0.814 MME 0.894 0.965 0 0.05 FGFBP2 0.814 FBXL13 0.795 0.914 0.013 0.008 FGFBP2 0.814 PPP1R2_PPP1R2P3 0.702 0.886 0.049 0.002 FGFBP2 0.814 CALM2_C2orf61 0.749 0.898 0.005 0.012 TMEM144_LOC2855 0.747 MTRR 0.674 0.879 0.002 0.006 TMEM144_LOC2855 0.747 HDHD1A 0.602 0.816 0.006 0.017 TMEM144_LOC2855 0.747 NPCDR1 0.726 0.862 0.013 0.016 TMEM144_LOC2855 0.747 ICAM1 0.675 0.842 0.01 0.03 TMEM144_LOC2855 0.747 TAF13 0.617 0.846 0.025 0.002 TMEM144_LOC2855 0.747 P4HA1_RPL17 0.664 0.877 0.003 0.003 TMEM144_LOC2855 0.747 C15orf54 0.72 0.837 0.015 0.028 TMEM144_LOC2855 0.747 KLHL5 0.657 0.858 0.007 0.011 TMEM144_LOC2855 0.747 KIAA0746 0.776 0.91 0 0.024 TMEM144_LOC2855 0.747 NA 0.7 0.885 0.011 0.001 TMEM144_LOC2855 0.747 E2F6 0.787 0.871 0.018 0.02 TMEM144_LOC2855 0.747 CD151 0.718 0.859 0.007 0.038 TMEM144_LOC2855 0.747 FSD1L 0.674 0.838 0.036 0.008 TMEM144_LOC2855 0.747 C7orf58 0.831 0.996 0 0 TMEM144_LOC2855 0.747 NP 0.726 0.874 0.001 0.043 TMEM144_LOC2855 0.747 IGJ 0.698 0.827 0.037 0.018 TMEM144_LOC2855 0.747 SDHC 0.736 0.888 0.014 0.002 TMEM144_LOC2855 0.747 NFXL1 0.728 0.869 0.003 0.018 CDS2 0.73 HSPC159 0.809 0.878 0.036 0.007 CDS2 0.73 FRMD3 0.755 0.866 0.006 0.012 CDS2 0.73 NA 0.927 0.959 0 0.041 CDS2 0.73 MTHFS 0.933 0.979 0 0.01 CDS2 0.73 OR9A2 0.823 0.873 0.015 0.028 CDS2 0.73 KIAA1257_ACAD9/ 0.851 0.907 0.001 0.037 CDS2 0.73 PDK4 0.863 0.918 0.003 0.005 CDS2 0.73 ZRANB1 0.897 0.942 0 0.024 CDS2 0.73 SYNE2 0.748 0.848 0.045 0.003 CDS2 0.73 FBXL13 0.795 0.854 0.041 0.027 CDS2 0.73 HIST2H2BF_HIST2 0.855 0.928 0.003 0.003 CDS2 0.73 SPATA6 0.812 0.882 0.015 0.037 BPI 0.817 LGALS1 0.722 0.86 0.029 0.029 BPI 0.817 PHOSPHO1 0.721 0.885 0.022 0.013 BPI 0.817 HIST1H3J 0.898 0.955 0.001 0.031 BPI 0.817 NEK6_LOC1001290 0.671 0.852 0.037 0.01 BPI 0.817 ICAM1 0.675 0.861 0.04 0.005 BPI 0.817 PTGS1 0.675 0.861 0.047 0.004 BPI 0.817 HIST1H3C 0.863 0.933 0.007 0.018 BPI 0.817 CLC 0.782 0.896 0.015 0.049 BPI 0.817 MPO 0.508 0.914 0.001 0 BPI 0.817 NP 0.726 0.857 0.042 0.037 BPI 0.817 TAS2R31 0.833 0.906 0.015 0.05 BPI 0.817 KIAA0101_CSNK1G 0.736 0.886 0.045 0.003 CCR3 0.869 TMTC1 0.809 0.949 0.036 0.001 CCR3 0.869 FRMD3 0.755 0.909 0.025 0.008 CCR3 0.869 NA 0.868 0.944 0.005 0.047 CCR3 0.869 SAP30 0.911 0.955 0.017 0.021 CCR3 0.869 AREG 0.849 0.965 0.021 0.002 CCR3 0.869 DAAM2_LOC100131 0.903 0.961 0.016 0.037 CCR3 0.869 IRS2 0.817 0.939 0.019 0.013 CCR3 0.869 OR9A2 0.823 0.932 0.01 0.014 CCR3 0.869 ACPL2 0.906 0.963 0.014 0.035 CCR3 0.869 NF-E4 0.861 0.933 0.018 0.047 CCR3 0.869 KIAA1257_ACAD9/ 0.851 0.942 0.02 0.012 CCR3 0.869 CCDC125 0.917 0.973 0.003 0.048 CCR3 0.869 PDK4 0.863 0.945 0.025 0.011 CCR3 0.869 RBP7 0.848 0.935 0.007 0.048 HSPC159 0.809 SLC39A9 0.776 0.887 0.003 0.045 HSPC159 0.809 GIMAP7 0.804 0.884 0.037 0.031 HSPC159 0.809 PHOSPHO1 0.721 0.896 0.047 0.002 HSPC159 0.809 SIAE 0.737 0.91 0.002 0.013 HSPC159 0.809 ITGA4_CERKL 0.88 0.961 0.001 0.013 HSPC159 0.809 EIF1AX_SCARNA9L 0.871 0.925 0.027 0.007 HSPC159 0.809 ZNF28 0.837 0.926 0.009 0.015 HSPC159 0.809 FSD1L_GARNL1 0.779 0.888 0.014 0.042 HSPC159 0.809 PTGS1 0.675 0.925 0.008 0 HSPC159 0.809 HIST1H3C 0.863 0.92 0.05 0.004 HSPC159 0.809 HIST1H3H 0.861 0.933 0.018 0.005 HSPC159 0.809 C7orf58 0.831 0.937 0.016 0.004 HSPC159 0.809 NP 0.726 0.893 0.011 0.004 PPP2R5A_SNORA16 0.953 SLC39A9 0.776 0.996 0.024 0 PPP2R5A_SNORA16 0.953 ANAPC11 0.943 0.996 0.033 0.009 PPP2R5A_SNORA16 0.953 INSIG1 0.957 0.999 0.018 0.028 PPP2R5A_SNORA16 0.953 HIST1H4L 0.961 0.996 0.034 0.028 PPP2R5A_SNORA16 0.953 C22orf37 0.924 0.993 0.026 0.011 PPP2R5A_SNORA16 0.953 VOPP1_LOC100128 0.938 0.992 0.026 0.016 PPP2R5A_SNORA16 0.953 PTGER2 0.881 0.99 0.046 0.002 PPP2R5A_SNORA16 0.953 PMS2CL_PMS2 0.942 1 0.019 0.015 PPP2R5A_SNORA16 0.953 IRF4 0.946 0.997 0.023 0.028 PPP2R5A_SNORA16 0.953 AMFR 0.867 0.987 0.05 0.004 PPP2R5A_SNORA16 0.953 HSP90AB1_HSP90A 0.924 0.996 0.027 0.01 PPP2R5A_SNORA16 0.953 ITGA4_CERKL 0.88 1 0.019 0.002 PPP2R5A_SNORA16 0.953 RRP12_LOC644215 0.904 0.99 0.047 0.006 PPP2R5A_SNORA16 0.953 TIMM10 0.949 0.989 0.03 0.031 PPP2R5A_SNORA16 0.953 FAM118A 0.882 0.99 0.048 0.005 PPP2R5A_SNORA16 0.953 IRF1 0.82 0.996 0.027 0 PPP2R5A_SNORA16 0.953 B4GALT3 0.917 0.999 0.019 0.013 PPP2R5A_SNORA16 0.953 CDC26 0.799 0.989 0.027 0 TMTC1 0.809 GIMAP7 0.804 0.911 0.007 0.011 TMTC1 0.809 LGALS1 0.722 0.896 0.002 0.009 TMTC1 0.809 DNAJC9_FAM149B1 0.944 0.983 0 0.042 TMTC1 0.809 HINT1 0.9 0.949 0.003 0.049 TMTC1 0.809 HLA-DPA1 0.796 0.898 0.006 0.035 TMTC1 0.809 NEK6_LOC1001290 0.671 0.855 0.033 0.007 TMTC1 0.809 GLT25D1 0.77 0.922 0 0.03 TMTC1 0.809 ITGA4_CERKL 0.88 0.946 0.001 0.048 TMTC1 0.809 ZNF28 0.837 0.916 0.005 0.034 TMTC1 0.809 KIAA0746 0.776 0.903 0.022 0.005 TMTC1 0.809 HIST1H3C 0.863 0.922 0.008 0.014 TMTC1 0.809 CD151 0.718 0.885 0.01 0.005 TMTC1 0.809 HIST1H3H 0.861 0.928 0.003 0.013 TMTC1 0.809 C7orf58 0.831 0.916 0.009 0.015 TMTC1 0.809 CTSL1_CTSLL3 0.676 0.879 0.047 0.003 TMTC1 0.809 NP 0.726 0.874 0.041 0.011 TMTC1 0.809 TMEM62_SPCS2_L 0.78 0.911 0.004 0.022 TMTC1 0.809 CDC26 0.799 0.897 0.005 0.048 TMTC1 0.809 TAS2R31 0.833 0.906 0.019 0.013 EAF2_HCG11_LOC 0.613 DNAJC9_FAM149B1 0.944 0.979 0 0.046 EAF2_HCG11_LOC 0.613 MTRR 0.674 0.861 0 0.002 EAF2_HCG11_LOC 0.613 P4HA1_RPL17 0.664 0.857 0 0 EAF2_HCG11_LOC 0.613 KLHL5 0.657 0.8 0.003 0.022 EAF2_HCG11_LOC 0.613 KIAA0746 0.776 0.899 0 0.006 EAF2_HCG11_LOC 0.613 NA 0.7 0.792 0.019 0.015 EAF2_HCG11_LOC 0.613 C7orf58 0.831 0.935 0 0.002 EAF2_HCG11_LOC 0.613 TMEM62_SPCS2_L 0.78 0.866 0 0.026 EAF2_HCG11_LOC 0.613 SDHC 0.736 0.801 0.006 0.033 RCBTB2_LOC10013 0.506 PDK4 0.863 0.933 0 0.038 RCBTB2_LOC10013 0.506 C7orf58 0.831 0.9 0 0.006 SEC24A_SAR1B 0.478 GIMAP7 0.804 0.889 0 0.043 SEC24A_SAR1B 0.478 MTRR 0.674 0.791 0 0.015 SEC24A_SAR1B 0.478 PMS2CL_PMS2 0.942 0.997 0 0.018 SEC24A_SAR1B 0.478 P4HA1_RPL17 0.664 0.763 0.001 0.028 SEC24A_SAR1B 0.478 KIAA0746 0.776 0.861 0 0.024 SEC24A_SAR1B 0.478 E2F6 0.787 0.823 0 0.038 SEC24A_SAR1B 0.478 SDHC 0.736 0.823 0 0.002 SH3PXD2B 0.9 LGALS2 0.922 0.976 0.019 0.048 SH3PXD2B 0.9 GALNT2 0.528 0.93 0.025 0 SH3PXD2B 0.9 GLT25D1 0.77 0.961 0.017 0.006 KLRD1 0.731 CCR1 0.693 0.808 0.047 0.012 KLRD1 0.731 PDE3B 0.567 0.799 0.035 0.007 KLRD1 0.731 NA 0.682 0.84 0.018 0.006 KLRD1 0.731 DLEU2_DLEU2L 0.711 0.86 0.001 0.017 KLRD1 0.731 ZNF587_ZNF417 0.632 0.828 0.009 0.005 KLRD1 0.731 SYNE2 0.748 0.904 0 0.019 KLRD1 0.731 PLEKHF2 0.667 0.808 0.041 0.041 KLRD1 0.731 LOC100128751 0.686 0.814 0.03 0.05 CHI3L1 0.722 OCR1 0.896 0.966 0 0.045 CHI3L1 0.722 DPH3 0.749 0.833 0.032 0.041 CHI3L1 0.722 NF-E4 0.861 0.97 0 0.006 CHI3L1 0.722 DLEU2_DLEU2L 0.711 0.82 0.032 0.022 CHI3L1 0.722 THBS1 0.849 0.916 0.001 0.038 CHI3L1 0.722 ZNF587_ZNF417 0.632 0.816 0.03 0.002 CHI3L1 0.722 SYNE2 0.748 0.888 0.003 0.004 CHI3L1 0.722 FBXL13 0.795 0.9 0.002 0.025 CHI3L1 0.722 HIST2H2BF_HIST2 0.855 0.932 0 0.047 FRMD3 0.755 SLC39A9 0.776 0.884 0.007 0.009 FRMD3 0.755 LGALS1 0.722 0.83 0.05 0.014 FRMD3 0.755 HINT1 0.9 0.926 0.003 0.048 FRMD3 0.755 HIST1H3J 0.898 0.93 0.002 0.027 FRMD3 0.755 HLA-DPA1 0.796 0.883 0.009 0.012 FRMD3 0.755 AMFR 0.867 0.96 0 0.006 FRMD3 0.755 RPL17_SNORD58B 0.917 0.952 0.001 0.022 FRMD3 0.755 ITGA4_CERKL 0.88 0.951 0 0.01 FRMD3 0.755 EIF1AX_SCARNA9L 0.871 0.907 0.003 0.026 FRMD3 0.755 ZNF28 0.837 0.913 0.005 0.007 FRMD3 0.755 HIST1H3B 0.89 0.934 0.002 0.019 FRMD3 0.755 FSD1L_GARNL1 0.779 0.877 0.028 0.005 FRMD3 0.755 HIST1H3C 0.863 0.891 0.027 0.011 FRMD3 0.755 HIST1H3H 0.861 0.897 0.017 0.01 FRMD3 0.755 S100B 0.841 0.877 0.019 0.041 FRMD3 0.755 IGK@_IGKC_IGKV 0.855 0.909 0.006 0.028 FRMD3 0.755 SON 0.856 0.926 0.001 0.015 FRMD3 0.755 TNFRSF17 0.875 0.922 0.003 0.041 FRMD3 0.755 CDC26 0.799 0.904 0.001 0.012 SLC39A9 0.776 NA 0.927 0.973 0 0.023 SLC39A9 0.776 EIF2AK2 0.964 0.997 0 0.037 SLC39A9 0.776 NA 0.868 0.944 0.001 0.008 SLC39A9 0.776 GPR65 0.736 0.898 0.011 0 SLC39A9 0.776 OR9A2 0.823 0.883 0.02 0.027 SLC39A9 0.776 ACPL2 0.906 0.97 0 0.039 SLC39A9 0.776 ERGIC1 0.886 0.949 0 0.029 SLC39A9 0.776 NF-E4 0.861 0.919 0.008 0.007 SLC39A9 0.776 HAL 0.942 0.982 0 0.05 SLC39A9 0.776 KIAA1257_ACAD9/ 0.851 0.951 0 0.001 SLC39A9 0.776 CCDC125 0.917 0.984 0 0.006 SLC39A9 0.776 PDK4 0.863 0.971 0 0 SLC39A9 0.776 THBS1 0.849 0.905 0.008 0.019 SLC39A9 0.776 ZRANB1 0.897 0.965 0 0.003 SLC39A9 0.776 MPZL3 0.892 0.956 0 0.046 SLC39A9 0.776 ZNF587_ZNF417 0.632 0.906 0.019 0 SLC39A9 0.776 CYP4F3_CYP4F2 0.901 0.956 0.001 0.022 SLC39A9 0.776 SYNE2 0.748 0.921 0.011 0 SLC39A9 0.776 MME 0.894 0.931 0.005 0.017 SLC39A9 0.776 CPM 0.854 0.915 0.005 0.025 SLC39A9 0.776 FBXL13 0.795 0.888 0.048 0.001 SLC39A9 0.776 HIST2H2BF_HIST2 0.855 0.928 0.006 0.007 SLC39A9 0.776 SPATA6 0.812 0.921 0.006 0.003 GIMAP7 0.804 NA 0.927 0.996 0 0.016 GIMAP7 0.804 NA 0.868 0.973 0 0.006 GIMAP7 0.804 GPR65 0.736 0.947 0 0 GIMAP7 0.804 OR9A2 0.823 0.896 0.012 0.023 GIMAP7 0.804 LRRFIP1 0.678 0.914 0.012 0 GIMAP7 0.804 CCDC125 0.917 0.991 0 0.015 GIMAP7 0.804 PDK4 0.863 0.968 0 0.003 GIMAP7 0.804 ZRANB1 0.897 0.968 0 0.015 GIMAP7 0.804 CYP4F3_CYP4F2 0.901 0.955 0.003 0.04 GIMAP7 0.804 SYNE2 0.748 0.939 0.001 0 GIMAP7 0.804 MME 0.894 0.956 0.003 0.017 GIMAP7 0.804 PLIN2 0.706 0.899 0.021 0.001 GIMAP7 0.804 FBXL13 0.795 0.898 0.046 0.007 GIMAP7 0.804 PPP1R2_PPP1R2P3 0.702 0.945 0.001 0 ANAPC11 0.943 NA 0.927 0.992 0.026 0.007 ANAPC11 0.943 SAP30 0.911 0.979 0.046 0.026 ANAPC11 0.943 ERGIC1 0.886 0.98 0.048 0.011 ANAPC11 0.943 HAL 0.942 0.984 0.024 0.033 ANAPC11 0.943 CCDC125 0.917 0.981 0.041 0.013 ANAPC11 0.943 MPZL3 0.892 0.974 0.024 0.008 ANAPC11 0.943 RGS2 0.915 0.975 0.023 0.023 NA 0.927 DNAJC9_FAM149B1 0.944 0.988 0.016 0.04 NA 0.927 LGALS2 0.922 0.979 0.032 0.015 NA 0.927 MTRR 0.674 0.974 0.021 0 NA 0.927 HLA-DPA1 0.796 0.979 0.012 0 NA 0.927 PTGER2 0.881 0.97 0.017 0.021 NA 0.927 PMS2CL_PMS2 0.942 0.997 0.01 0.021 NA 0.927 TRAF3 0.87 0.989 0.013 0.006 NA 0.927 AMFR 0.867 0.963 0.029 0.026 NA 0.927 RPL17_SNORD58B 0.917 0.988 0.039 0.007 NA 0.927 HSP90AB1_HSP90A 0.924 0.996 0.014 0.013 NA 0.927 ITGA4_CERKL 0.88 0.99 0.006 0.007 NA 0.927 CD300A 0.841 0.954 0.04 0.007 NA 0.927 P4HA1_RPL17 0.664 0.97 0.048 0 NA 0.927 KLHL5 0.657 0.981 0.042 0 NA 0.927 MGST3 0.857 0.975 0.028 0.002 NA 0.927 KIAA0746 0.776 0.993 0.019 0 NA 0.927 CCR4 0.871 0.989 0.028 0.003 NA 0.927 ABCG1 0.85 0.965 0.039 0.003 NA 0.927 FAM118A 0.882 0.974 0.022 0.02 NA 0.927 IGK@_IGKC_IGKV 0.855 0.992 0.025 0.001 NA 0.927 IRF1 0.82 0.965 0.016 0.003 NA 0.927 C7orf58 0.831 0.989 0.037 0 NA 0.927 B4GALT3 0.917 0.984 0.008 0.039 NA 0.927 TNFRSF17 0.875 0.984 0.043 0.001 NA 0.927 DCTN5 0.722 0.975 0.015 0 INSIG1 0.957 PRR13_PCBP2 0.947 0.993 0.04 0.015 INSIG1 0.957 MTHFS 0.933 0.999 0.024 0.019 INSIG1 0.957 EIF2AK2 0.964 0.999 0.028 0.033 INSIG1 0.957 CDA 0.945 0.996 0.042 0.015 INSIG1 0.957 GAB2 0.969 0.996 0.033 0.045 INSIG1 0.957 HAL 0.942 0.994 0.037 0.011 INSIG1 0.957 MPZL3 0.892 0.993 0.027 0.003 INSIG1 0.957 RGS2 0.915 0.997 0.023 0.003 FOLR3_FOLR2 0.78 HIST1H3J 0.898 0.943 0.001 0.05 FOLR3_FOLR2 0.78 HIST1H3A 0.908 0.962 0.001 0.015 FOLR3_FOLR2 0.78 GLT25D1 0.77 0.926 0.002 0.012 FOLR3_FOLR2 0.78 HIST1H3B 0.89 0.943 0.001 0.05 FOLR3_FOLR2 0.78 HIST1H3C 0.863 0.915 0.013 0.02 FOLR3_FOLR2 0.78 HIST1H3H 0.861 0.92 0.006 0.023 PRR13_PCBP2 0.947 LGALS2 0.922 0.989 0.035 0.003 PRR13_PCBP2 0.947 C22orf37 0.924 0.985 0.024 0.021 PRR13_PCBP2 0.947 VOPP1_LOC100128 0.938 0.981 0.028 0.017 PRR13_PCBP2 0.947 PMS2CL_PMS2 0.942 0.992 0.019 0.019 PRR13_PCBP2 0.947 IRF4 0.946 0.994 0.015 0.032 PRR13_PCBP2 0.947 HSP90AB1_HSP90A 0.924 0.988 0.05 0.007 PRR13_PCBP2 0.947 CD300A 0.841 0.985 0.038 0 PRR13_PCBP2 0.947 RRP12_LOC644215 0.904 0.989 0.025 0.008 PRR13_PCBP2 0.947 TIMM10 0.949 0.993 0.017 0.022 PRR13_PCBP2 0.947 IRF1 0.82 0.985 0.042 0 PRR13_PCBP2 0.947 LASS4 0.924 0.979 0.018 0.041 PRR13_PCBP2 0.947 B4GALT3 0.917 0.991 0.024 0.014 PRR13_PCBP2 0.947 CDC26 0.799 0.982 0.035 0 PRR13_PCBP2 0.947 IGL@_IGLV1-44_(—) 0.953 0.992 0.018 0.033 HIST1H4L 0.961 SAP30 0.911 0.988 0.018 0.036 HIST1H4L 0.961 IRS2 0.817 0.985 0.023 0.002 HIST1H4L 0.961 ERGIC1 0.886 0.98 0.048 0.026 HIST1H4L 0.961 MPZL3 0.892 0.979 0.027 0.012 HIST1H4L 0.961 MYL9 0.641 0.992 0.038 0 HIST1H4L 0.961 RBP7 0.848 0.992 0.038 0 HIST1H4L 0.961 RGS2 0.915 0.985 0.017 0.022 LGALS1 0.722 RETN 0.694 0.851 0.014 0.011 LGALS1 0.722 GPR65 0.736 0.853 0.015 0.011 LGALS1 0.722 OR9A2 0.823 0.888 0.004 0.028 LGALS1 0.722 KCNMA1 0.707 0.825 0.037 0.007 LGALS1 0.722 PDK4 0.863 0.948 0 0.003 LGALS1 0.722 LHFP 0.731 0.814 0.032 0.039 LGALS1 0.722 FABP2 0.744 0.832 0.05 0.023 LGALS1 0.722 RBP7 0.848 0.902 0.002 0.039 CCR1 0.693 MTRR 0.674 0.854 0.005 0.012 CCR1 0.693 P4HA1_RPL17 0.664 0.824 0.017 0.012 CCR1 0.693 KLHL5 0.657 0.808 0.019 0.044 CCR1 0.693 LAIR1_LAIR2 0.607 0.802 0.043 0.014 CCR1 0.693 PYHIN1 0.686 0.805 0.021 0.022 CCR1 0.693 C7orf58 0.831 0.939 0 0.004 CCR1 0.693 SDHC 0.736 0.834 0.04 0.008 CCR1 0.693 NFXL1 0.728 0.827 0.013 0.039 TPST1 0.92 PHOSPHO1 0.721 0.985 0.047 0 TPST1 0.92 HIST1H3J 0.898 0.979 0.046 0.008 TPST1 0.92 HIST1H3A 0.908 0.979 0.039 0.018 TPST1 0.92 TYMS 0.863 0.957 0.037 0.042 TPST1 0.92 ABCG1 0.85 0.966 0.043 0.018 TPST1 0.92 HIST1H3B 0.89 0.981 0.037 0.013 TPST1 0.92 TNFRSF17 0.875 0.964 0.036 0.035 HLA-DRA 0.855 NA 0.868 0.946 0.009 0.04 HLA-DRA 0.855 GPR65 0.736 0.911 0.022 0.001 HLA-DRA 0.855 ACPL2 0.906 0.96 0.031 0.01 HLA-DRA 0.855 KIAA1257_ACAD9/ 0.851 0.937 0.042 0.011 HLA-DRA 0.855 PDK4 0.863 0.985 0.005 0.001 HLA-DRA 0.855 PLIN2 0.706 0.965 0.024 0 FFAR2 0.637 LAIR1_LAIR2 0.607 0.759 0.021 0.029 FFAR2 0.637 C7orf58 0.831 0.905 0 0.045 PHOSPHO1 0.721 ABCA13 0.743 0.895 0.003 0.005 PHOSPHO1 0.721 DAAM2_LOC100131 0.903 0.986 0 0.016 PHOSPHO1 0.721 IRS2 0.817 0.96 0 0.015 PHOSPHO1 0.721 DPH3 0.749 0.886 0.004 0.003 PHOSPHO1 0.721 NF-E4 0.861 0.926 0.001 0.049 PHOSPHO1 0.721 DLEU2_DLEU2L 0.711 0.819 0.044 0.02 PHOSPHO1 0.721 THBS1 0.849 0.952 0 0.003 PHOSPHO1 0.721 MYL9 0.641 0.832 0.023 0.008 PHOSPHO1 0.721 FBXL13 0.795 0.898 0.005 0.019 PHOSPHO1 0.721 RBP7 0.848 0.942 0 0.018 PHOSPHO1 0.721 CALM2_C2orf61 0.749 0.895 0.004 0.001 PPIF 0.934 MTHFS 0.933 0.997 0.048 0.021 MTHFS 0.933 DNAJC9_FAM149B1 0.944 0.992 0.032 0.023 MTHFS 0.933 LGALS2 0.922 0.994 0.031 0.003 MTHFS 0.933 C22orf37 0.924 0.979 0.033 0.042 MTHFS 0.933 PTGER2 0.881 0.979 0.043 0.004 MTHFS 0.933 PMS2CL_PMS2 0.942 0.994 0.028 0.012 MTHFS 0.933 HSP90AB1_HSP90A 0.924 0.996 0.026 0.01 MTHFS 0.933 CD300A 0.841 0.97 0.029 0.002 MTHFS 0.933 MGST3 0.857 0.996 0.021 0.001 MTHFS 0.933 RRP12_LOC644215 0.904 0.989 0.022 0.011 MTHFS 0.933 TIMM10 0.949 0.993 0.022 0.027 MTHFS 0.933 FAM118A 0.882 0.982 0.049 0.009 MTHFS 0.933 IRF1 0.82 0.988 0.031 0 MTHFS 0.933 B4GALT3 0.917 0.984 0.03 0.029 MTHFS 0.933 ATP6V0D1_LOC100 0.567 0.969 0.01 0 MTHFS 0.933 CDC26 0.799 0.972 0.031 0 DNAJC9_FAM149B1 0.944 EIF2AK2 0.964 0.997 0.023 0.044 DNAJC9_FAM149B1 0.944 NA 0.868 0.98 0.035 0.002 DNAJC9_FAM149B1 0.944 HIST1H2AA 0.903 0.974 0.035 0.018 DNAJC9_FAM149B1 0.944 GPR65 0.736 0.975 0.048 0 DNAJC9_FAM149B1 0.944 ACPL2 0.906 0.988 0.024 0.022 DNAJC9_FAM149B1 0.944 LRRFIP1 0.678 0.983 0.041 0 DNAJC9_FAM149B1 0.944 TRIM21 0.538 0.974 0.049 0 DNAJC9_FAM149B1 0.944 HAL 0.942 0.989 0.021 0.037 DNAJC9_FAM149B1 0.944 CCDC125 0.917 0.991 0.016 0.016 DNAJC9_FAM149B1 0.944 PDK4 0.863 0.996 0.02 0.001 DNAJC9_FAM149B1 0.944 ZRANB1 0.897 0.988 0.018 0.006 DNAJC9_FAM149B1 0.944 MPZL3 0.892 0.974 0.03 0.024 DNAJC9_FAM149B1 0.944 CYP4F3_CYP4F2 0.901 0.991 0.027 0.017 DNAJC9_FAM149B1 0.944 SYNE2 0.748 0.986 0.023 0 DNAJC9_FAM149B1 0.944 RBP7 0.848 0.984 0.036 0.001 DNAJC9_FAM149B1 0.944 RGS2 0.915 0.98 0.019 0.025 DNAJC9_FAM149B1 0.944 PPP1R2_PPP1R2P3 0.702 0.981 0.047 0 DNAJC9_FAM149B1 0.944 HIST2H2BF_HIST2 0.855 0.992 0.03 0.002 LCN2 0.653 HIST1H2AJ_HIST1 0.77 0.82 0.033 0.005 LCN2 0.653 HIST1H3C 0.863 0.924 0 0.048 LCN2 0.653 MPO 0.508 0.74 0.012 0.001 EIF2AK2 0.964 LGALS2 0.922 0.99 0.048 0.006 EIF2AK2 0.964 VOPP1_LOC100128 0.938 0.993 0.03 0.019 EIF2AK2 0.964 PTGER2 0.881 0.997 0.037 0.002 EIF2AK2 0.964 PMS2CL_PMS2 0.942 1 0.032 0.015 EIF2AK2 0.964 TRAF3 0.87 0.995 0.048 0.004 EIF2AK2 0.964 IRF4 0.946 1 0.032 0.023 EIF2AK2 0.964 HSP90AB1_HSP90A 0.924 0.999 0.031 0.01 EIF2AK2 0.964 ITGA4_CERKL 0.88 0.998 0.037 0.002 EIF2AK2 0.964 CD300A 0.841 0.997 0.035 0 EIF2AK2 0.964 TRIM21 0.538 0.993 0.042 0 EIF2AK2 0.964 KIAA0746 0.776 0.996 0.044 0 EIF2AK2 0.964 TIMM10 0.949 0.997 0.034 0.019 EIF2AK2 0.964 IGK@_IGKC_IGKV 0.855 0.994 0.043 0.001 EIF2AK2 0.964 IRF1 0.82 0.999 0.032 0 EIF2AK2 0.964 B4GALT3 0.917 1 0.032 0.012 EIF2AK2 0.964 DCTN5 0.722 0.99 0.035 0 LGALS2 0.922 HIST1H2AA 0.903 0.977 0.013 0.009 LGALS2 0.922 ACPL2 0.906 0.961 0.045 0.041 LGALS2 0.922 ERGIC1 0.886 0.961 0.021 0.037 LGALS2 0.922 CCDC125 0.917 0.974 0.009 0.027 LGALS2 0.922 ZRANB1 0.897 0.971 0.016 0.007 LGALS2 0.922 MPZL3 0.892 0.974 0.003 0.02 LGALS2 0.922 KDM6B_TMEM88 0.869 0.971 0.019 0.019 LGALS2 0.922 RBP7 0.848 0.958 0.048 0.001 LGALS2 0.922 RGS2 0.915 0.978 0.004 0.03 SIAE 0.737 GPR65 0.736 0.844 0.048 0.032 SIAE 0.737 PDK4 0.863 0.924 0.005 0.024 AP3B2 0.756 HIST1H3J 0.898 0.926 0.005 0.01 AP3B2 0.756 HIST1H3A 0.908 0.944 0.002 0.002 AP3B2 0.756 GLT25D1 0.77 0.875 0.036 0.027 AP3B2 0.756 HIST1H2BM 0.891 0.928 0.004 0.018 AP3B2 0.756 HIST1H3B 0.89 0.924 0.006 0.006 AP3B2 0.756 HIST1H3C 0.863 0.895 0.03 0.006 AP3B2 0.756 HIST1H3H 0.861 0.903 0.021 0.005 AP3B2 0.756 APOLD1 0.871 0.924 0.003 0.012 AP3B2 0.756 C7orf58 0.831 0.886 0.028 0.05 AP3B2 0.756 CDC26 0.799 0.859 0.04 0.05 ABCA13 0.743 HIST1H3J 0.898 0.962 0 0.002 ABCA13 0.743 AGTRAP 0.611 0.811 0.049 0.003 ABCA13 0.743 HIST1H3A 0.908 0.97 0 0.006 ABCA13 0.743 CENPK 0.834 0.879 0.029 0.036 ABCA13 0.743 HIST1H3B 0.89 0.978 0 0.004 ABCA13 0.743 HIST1H3C 0.863 0.933 0.003 0.002 ABCA13 0.743 HIST1H3H 0.861 0.934 0.001 0.004 ABCA13 0.743 TPX2 0.635 0.833 0.035 0.002 ABCA13 0.743 MPO 0.508 0.853 0.028 0 ABCA13 0.743 KIAA0101_CSNK1G 0.736 0.875 0.018 0.001 NA 0.868 HINT1 0.9 0.956 0.025 0.013 NA 0.868 HLA-DPA1 0.796 0.951 0.009 0.001 NA 0.868 PMS2CL_PMS2 0.942 0.984 0.001 0.039 NA 0.868 SULF2 0.591 0.931 0.033 0 NA 0.868 TRAF3 0.87 0.993 0.001 0.004 NA 0.868 IRF4 0.946 0.996 0.001 0.035 NA 0.868 RPL17_SNORD58B 0.917 0.947 0.047 0.02 NA 0.868 HSP90AB1_HSP90A 0.924 0.996 0.001 0.01 NA 0.868 ITGA4_CERKL 0.88 0.981 0.002 0.002 NA 0.868 CD300A 0.841 0.928 0.011 0.023 NA 0.868 MGST3 0.857 0.941 0.014 0.023 NA 0.868 KIAA0746 0.776 0.969 0.011 0 NA 0.868 CCR4 0.871 0.965 0.015 0.001 NA 0.868 ABCG1 0.85 0.947 0.005 0.018 NA 0.868 FAM118A 0.882 0.947 0.025 0.025 NA 0.868 IGK@_IGKC_IGKV 0.855 0.957 0.03 0.001 NA 0.868 IRF1 0.82 0.932 0.013 0.008 NA 0.868 TNFRSF17 0.875 0.958 0.024 0.002 NA 0.868 DCTN5 0.722 0.947 0.041 0 EFCAB2 0.773 AIG1 0.56 0.829 0.029 0.001 EFCAB2 0.773 HS2ST1_UBA2 0.668 0.829 0.012 0.048 EFCAB2 0.773 NEK6_LOC1001290 0.671 0.832 0.021 0.04 EFCAB2 0.773 MINPP1 0.7 0.857 0.038 0.005 EFCAB2 0.773 C15orf54 0.72 0.875 0.004 0.007 EFCAB2 0.773 PTGS1 0.675 0.855 0.011 0.012 EFCAB2 0.773 E2F6 0.787 0.882 0.021 0.012 EFCAB2 0.773 CD151 0.718 0.843 0.036 0.031 EFCAB2 0.773 TPX2 0.635 0.823 0.037 0.009 EFCAB2 0.773 HIST1H2BJ 0.703 0.854 0.042 0.022 EFCAB2 0.773 C7orf58 0.831 0.937 0.003 0.004 EFCAB2 0.773 NP 0.726 0.862 0.008 0.043 EFCAB2 0.773 SDHC 0.736 0.899 0.014 0.001 EFCAB2 0.773 NFXL1 0.728 0.865 0.048 0.006 EFCAB2 0.773 KIAA0101_CSNK1G 0.736 0.872 0.009 0.01 HIST1H2AA 0.903 C22orf37 0.924 0.978 0.006 0.015 HIST1H2AA 0.903 IRF4 0.946 0.98 0.01 0.047 HIST1H2AA 0.903 HSP90AB1_HSP90A 0.924 0.964 0.032 0.043 HIST1H2AA 0.903 TYMS 0.863 0.959 0.041 0.002 HIST1H2AA 0.903 RRP12_LOC644215 0.904 0.959 0.029 0.03 HINT1 0.9 GPR65 0.736 0.95 0.035 0 HINT1 0.9 OCR1 0.896 0.978 0.008 0.042 HINT1 0.9 KIAA1257_ACAD9/ 0.851 0.932 0.049 0.04 HINT1 0.9 PDK4 0.863 0.978 0.014 0.002 HINT1 0.9 ZRANB1 0.897 0.963 0.014 0.035 HINT1 0.9 CYP4F3_CYP4F2 0.901 0.978 0.008 0.046 HINT1 0.9 SYNE2 0.748 0.962 0.006 0 HINT1 0.9 MME 0.894 0.971 0.018 0.034 HINT1 0.9 PLIN2 0.706 0.938 0.049 0 HINT1 0.9 RBP7 0.848 0.963 0.005 0.013 HINT1 0.9 PPP1R2_PPP1R2P3 0.702 0.953 0.033 0 HIST1H3J 0.898 PCOLCE2 0.567 0.925 0.008 0 HIST1H3J 0.898 AREG 0.849 0.961 0.003 0.007 HIST1H3J 0.898 DAAM2_LOC100131 0.903 0.977 0.003 0.023 HIST1H3J 0.898 IRS2 0.817 0.955 0.005 0.009 HIST1H3J 0.898 OR9A2 0.823 0.943 0.03 0.004 HIST1H3J 0.898 KCNMA1 0.707 0.93 0.032 0 HIST1H3J 0.898 DPH3 0.749 0.951 0.045 0 HIST1H3J 0.898 NF-E4 0.861 0.957 0.027 0.01 HIST1H3J 0.898 THBS1 0.849 0.956 0.026 0.003 HIST1H3J 0.898 LHFP 0.731 0.928 0.044 0.001 HIST1H3J 0.898 ZRANB1 0.897 0.964 0.023 0.018 HIST1H3J 0.898 FABP2 0.744 0.928 0.026 0.001 HIST1H3J 0.898 MYL9 0.641 0.952 0.01 0 HIST1H3J 0.898 RBP7 0.848 0.973 0.002 0.004 HIST1H3J 0.898 HIST2H2BF_HIST2 0.855 0.939 0.048 0.028 HIST1H3J 0.898 CALM2_C2orf61 0.749 0.953 0.041 0 CDA 0.945 C22orf37 0.924 0.986 0.05 0.015 CDA 0.945 VOPP1_LOC100128 0.938 0.985 0.014 0.025 CDA 0.945 PMS2CL_PMS2 0.942 0.998 0.022 0.017 CDA 0.945 HSP90AB1_HSP90A 0.924 0.997 0.02 0.011 CDA 0.945 MGST3 0.857 0.989 0.031 0.001 CDA 0.945 RRP12_LOC644215 0.904 0.996 0.022 0.006 CDA 0.945 TIMM10 0.949 0.986 0.031 0.033 CDA 0.945 IRF1 0.82 0.994 0.033 0 CDA 0.945 LASS4 0.924 0.993 0.018 0.019 CDA 0.945 B4GALT3 0.917 0.992 0.031 0.012 CDA 0.945 CDC26 0.799 0.981 0.04 0 SAP30 0.911 C22orf37 0.924 0.976 0.017 0.035 SAP30 0.911 TYMS 0.863 0.972 0.047 0.001 SAP30 0.911 ABCG1 0.85 0.978 0.014 0.005 SAP30 0.911 TIMM10 0.949 0.98 0.036 0.034 SAP30 0.911 B4GALT3 0.917 0.976 0.036 0.02 AGTRAP 0.611 HIST1H3A 0.908 0.93 0 0.03 AGTRAP 0.611 RBP7 0.848 0.926 0 0.007 MTRR 0.674 LRRFIP1 0.678 0.848 0.002 0.004 MTRR 0.674 DLEU2_DLEU2L 0.711 0.813 0.005 0.045 MTRR 0.674 PDK4 0.863 0.953 0 0.017 MTRR 0.674 ZNF587_ZNF417 0.632 0.807 0.009 0.007 MTRR 0.674 PLEKHF2 0.667 0.807 0.018 0.021 MTRR 0.674 IL1RL1 0.738 0.842 0.006 0.026 MTRR 0.674 AIF1 0.618 0.788 0.027 0.024 MTRR 0.674 PPP1R2_PPP1R2P3 0.702 0.836 0.003 0.032 PCOLCE2 0.567 HIST1H3A 0.908 0.936 0 0.025 PCOLCE2 0.567 HIST1H2BM 0.891 0.924 0 0.023 PCOLCE2 0.567 HIST1H3B 0.89 0.916 0 0.012 PCOLCE2 0.567 HIST1H3C 0.863 0.894 0 0.011 PCOLCE2 0.567 HIST1H3H 0.861 0.898 0 0.011 PCOLCE2 0.567 CMTM5 0.671 0.738 0.05 0.033 HS2ST1_UBA2 0.668 KCNMA1 0.707 0.808 0.019 0.039 HS2ST1_UBA2 0.668 DLEU2_DLEU2L 0.711 0.806 0.04 0.017 HS2ST1_UBA2 0.668 PDK4 0.863 0.914 0 0.049 HS2ST1_UBA2 0.668 SYNE2 0.748 0.848 0.005 0.033 HS2ST1_UBA2 0.668 FBXL13 0.795 0.857 0.005 0.033 HIST1H3A 0.908 AREG 0.849 0.967 0.005 0.005 HIST1H3A 0.908 DAAM2_LOC100131 0.903 0.98 0.004 0.027 HIST1H3A 0.908 IRS2 0.817 0.961 0.003 0.007 HIST1H3A 0.908 ACPL2 0.906 0.967 0.014 0.043 HIST1H3A 0.908 DPH3 0.749 0.958 0.039 0 HIST1H3A 0.908 NF-E4 0.861 0.959 0.019 0.01 HIST1H3A 0.908 DLEU2_DLEU2L 0.711 0.965 0.032 0 HIST1H3A 0.908 PDK4 0.863 0.949 0.035 0.02 HIST1H3A 0.908 THBS1 0.849 0.966 0.036 0.001 HIST1H3A 0.908 LHFP 0.731 0.94 0.028 0 HIST1H3A 0.908 ZRANB1 0.897 0.961 0.028 0.024 HIST1H3A 0.908 FABP2 0.744 0.943 0.041 0 HIST1H3A 0.908 MYL9 0.641 0.946 0.046 0 HIST1H3A 0.908 RBP7 0.848 0.966 0.006 0.007 HIST1H3A 0.908 HIST2H2BF_HIST2 0.855 0.955 0.048 0.009 C22orf37 0.924 ERGIC1 0.886 0.978 0.047 0.008 C22orf37 0.924 HAL 0.942 0.988 0.024 0.01 C22orf37 0.924 CCDC125 0.917 0.984 0.033 0.006 C22orf37 0.924 MPZL3 0.892 0.973 0.007 0.014 C22orf37 0.924 RGS2 0.915 0.975 0.028 0.011 HLA-DPA1 0.796 GPR65 0.736 0.9 0.031 0 HLA-DPA1 0.796 OR9A2 0.823 0.889 0.028 0.029 HLA-DPA1 0.796 KIAA1257_ACAD9/ 0.851 0.914 0.002 0.035 HLA-DPA1 0.796 PDK4 0.863 0.966 0.001 0.001 HLA-DPA1 0.796 THBS1 0.849 0.895 0.022 0.047 HLA-DPA1 0.796 PLIN2 0.706 0.892 0.04 0.001 HLA-DPA1 0.796 HIST2H2BF_HIST2 0.855 0.912 0.019 0.029 VOPP1_LOC100128 0.938 HAL 0.942 0.98 0.033 0.012 VOPP1_LOC100128 0.938 MPZL3 0.892 0.981 0.021 0.005 VOPP1_LOC100128 0.938 RGS2 0.915 0.985 0.019 0.006 SLC39A8 0.825 GPR65 0.736 0.917 0.007 0.005 SLC39A8 0.825 PDK4 0.863 0.935 0.01 0.036 SLC39A8 0.825 SPATA6 0.812 0.894 0.013 0.035 MKI67 0.621 HIST1H3C 0.863 0.897 0 0.021 AREG 0.849 GLT25D1 0.77 0.918 0.024 0.011 AREG 0.849 CD300A 0.841 0.905 0.039 0.025 AREG 0.849 ZNF28 0.837 0.915 0.041 0.023 AREG 0.849 HIST1H2BM 0.891 0.947 0.009 0.045 AREG 0.849 HIST1H3B 0.89 0.949 0.017 0.006 AREG 0.849 HIST1H3C 0.863 0.942 0.032 0.001 AREG 0.849 HIST1H3H 0.861 0.951 0.017 0.001 AREG 0.849 APOLD1 0.871 0.937 0.016 0.033 AREG 0.849 CLC 0.782 0.937 0.026 0.002 AREG 0.849 CDC26 0.799 0.912 0.036 0.011 DAAM2_LOC100131 0.903 GLT25D1 0.77 0.965 0.033 0.002 DAAM2_LOC100131 0.903 CD300A 0.841 0.958 0.032 0.003 DAAM2_LOC100131 0.903 TYMS 0.863 0.965 0.012 0.014 DAAM2_LOC100131 0.903 RRP12_LOC644215 0.904 0.969 0.022 0.048 DAAM2_LOC100131 0.903 HIST1H2BM 0.891 0.971 0.021 0.026 DAAM2_LOC100131 0.903 ABCG1 0.85 0.977 0.022 0.006 DAAM2_LOC100131 0.903 HIST1H3B 0.89 0.986 0.018 0.004 DAAM2_LOC100131 0.903 APOLD1 0.871 0.971 0.019 0.008 DAAM2_LOC100131 0.903 SON 0.856 0.955 0.036 0.022 DAAM2_LOC100131 0.903 IRF1 0.82 0.946 0.014 0.008 DAAM2_LOC100131 0.903 TNFRSF17 0.875 0.968 0.019 0.012 DAAM2_LOC100131 0.903 CDC26 0.799 0.965 0.023 0.001 DAAM2_LOC100131 0.903 GLDC 0.714 0.966 0.042 0 TREML1 0.634 CENPK 0.834 0.88 0 0.049 TREML1 0.634 MINPP1 0.7 0.795 0.017 0.044 TREML1 0.634 ITGA2B 0.58 0.827 0 0.002 TREML1 0.634 HIST1H2AJ_HIST1 0.77 0.803 0.039 0.01 TREML1 0.634 PTGS1 0.675 0.909 0 0.001 TREML1 0.634 HIST1H3C 0.863 0.916 0 0.017 TREML1 0.634 E2F6 0.787 0.856 0.001 0.033 TREML1 0.634 HIST1H3H 0.861 0.936 0 0.015 TREML1 0.634 HIST1H2BJ 0.703 0.838 0.001 0.03 TREML1 0.634 RPIA 0.601 0.761 0.049 0.022 TREML1 0.634 CMTM5 0.671 0.828 0 0.045 TREML1 0.634 SDHC 0.736 0.814 0.005 0.044 TREML1 0.634 KIAA0101_CSNK1G 0.736 0.836 0.001 0.02 PTGER2 0.881 GPR65 0.736 0.96 0.032 0 PTGER2 0.881 ACPL2 0.906 0.969 0.012 0.032 PTGER2 0.881 HAL 0.942 0.976 0.006 0.039 PTGER2 0.881 CCDC125 0.917 0.968 0.011 0.021 PTGER2 0.881 PDK4 0.863 0.976 0.011 0.001 PTGER2 0.881 ZRANB1 0.897 0.973 0.012 0.003 PTGER2 0.881 MPZL3 0.892 0.962 0.002 0.05 PTGER2 0.881 RBP7 0.848 0.942 0.05 0.006 PTGER2 0.881 RGS2 0.915 0.974 0.003 0.021 CEACAM8 0.647 HIST1H2AJ_HIST1 0.77 0.823 0.019 0.006 CEACAM8 0.647 DEFA4_DEFA8P 0.481 0.824 0.004 0 CEACAM8 0.647 MPO 0.508 0.732 0 0 PMS2CL_PMS2 0.942 ACPL2 0.906 0.999 0.017 0.007 PMS2CL_PMS2 0.942 ERGIC1 0.886 0.98 0.043 0.012 PMS2CL_PMS2 0.942 HAL 0.942 0.992 0.015 0.019 PMS2CL_PMS2 0.942 CCDC125 0.917 0.996 0.016 0.008 PMS2CL_PMS2 0.942 PDK4 0.863 0.991 0.041 0.001 PMS2CL_PMS2 0.942 ZRANB1 0.897 0.995 0.022 0.002 PMS2CL_PMS2 0.942 MPZL3 0.892 0.983 0.015 0.007 PMS2CL_PMS2 0.942 SYNE2 0.748 0.983 0.037 0 PMS2CL_PMS2 0.942 RBP7 0.848 0.984 0.047 0.001 PMS2CL_PMS2 0.942 RGS2 0.915 0.99 0.016 0.007 RETN 0.694 GLT25D1 0.77 0.842 0.031 0.044 PDE3B 0.567 TAS2R31 0.833 0.891 0 0.03 SULF2 0.591 ZNF587_ZNF417 0.632 0.759 0.017 0.005 NEK6_LOC1001290 0.671 OR9A2 0.823 0.878 0.001 0.045 NEK6_LOC1001290 0.671 KCNMA1 0.707 0.815 0.013 0.013 NEK6_LOC1001290 0.671 PDK4 0.863 0.924 0 0.004 NEK6_LOC1001290 0.671 FABP2 0.744 0.823 0.033 0.023 CENPK 0.834 GPR65 0.736 0.895 0.006 0.015 CENPK 0.834 DPH3 0.749 0.907 0.008 0.006 CENPK 0.834 NA 0.682 0.925 0.013 0 CENPK 0.834 DLEU2_DLEU2L 0.711 0.92 0.022 0 CENPK 0.834 LHFP 0.731 0.86 0.048 0.036 CENPK 0.834 MYL9 0.641 0.869 0.027 0.002 CENPK 0.834 SYNE2 0.748 0.926 0.001 0.005 CENPK 0.834 FBXL13 0.795 0.912 0.019 0.015 CENPK 0.834 CALM2_C2orf61 0.749 0.914 0.011 0.004 TRAF3 0.87 GPR65 0.736 0.966 0.012 0 TRAF3 0.87 CCDC125 0.917 0.983 0.004 0.023 TRAF3 0.87 PDK4 0.863 0.967 0.017 0.003 TRAF3 0.87 ZRANB1 0.897 0.97 0.005 0.02 GPR65 0.736 HSP90AB1_HSP90A 0.924 0.975 0 0.021 GPR65 0.736 GLT25D1 0.77 0.866 0.01 0.016 GPR65 0.736 ITGA4_CERKL 0.88 0.969 0 0.015 GPR65 0.736 EIF1AX_SCARNA9L 0.871 0.935 0 0.008 GPR65 0.736 ZNF28 0.837 0.916 0 0.019 GPR65 0.736 MGST3 0.857 0.934 0 0.022 GPR65 0.736 KIAA0746 0.776 0.891 0.012 0.001 GPR65 0.736 HSPB1_HSPBL2 0.705 0.823 0.047 0.048 GPR65 0.736 CCR4 0.871 0.936 0.001 0.008 GPR65 0.736 FSD1L_GARNL1 0.779 0.854 0.014 0.036 GPR65 0.736 CD151 0.718 0.843 0.044 0.004 GPR65 0.736 IGK@_IGKC_IGKV 0.855 0.906 0.002 0.042 GPR65 0.736 C7orf58 0.831 0.914 0.006 0.001 GPR65 0.736 NP 0.726 0.895 0.008 0.001 GPR65 0.736 TMEM62_SPCS2_L 0.78 0.872 0.026 0.005 GPR65 0.736 TAS2R31 0.833 0.902 0.005 0.005 GPR65 0.736 IGLV6-57 0.761 0.839 0.044 0.014 GPR65 0.736 DCTN5 0.722 0.858 0.016 0.003 IRF4 0.946 HAL 0.942 0.989 0.036 0.016 IRF4 0.946 CCDC125 0.917 0.995 0.02 0.008 IRF4 0.946 RGS2 0.915 0.99 0.045 0.003 AMFR 0.867 ACPL2 0.906 0.979 0.014 0.011 AMFR 0.867 ZRANB1 0.897 0.971 0.022 0.002 RPL17_SNORD58B 0.917 ACPL2 0.906 0.981 0.013 0.029 RPL17_SNORD58B 0.917 OCR1 0.896 0.979 0.021 0.026 RPL17_SNORD58B 0.917 NF-E4 0.861 0.977 0.034 0.003 RPL17_SNORD58B 0.917 KIAA1257_ACAD9/ 0.851 0.958 0.029 0.01 RPL17_SNORD58B 0.917 PDK4 0.863 0.978 0.022 0.003 RPL17_SNORD58B 0.917 ZRANB1 0.897 0.969 0.041 0.024 RPL17_SNORD58B 0.917 CYP4F3_CYP4F2 0.901 0.979 0.012 0.031 RPL17_SNORD58B 0.917 SYNE2 0.748 0.967 0.01 0 RPL17_SNORD58B 0.917 MME 0.894 0.969 0.027 0.038 RPL17_SNORD58B 0.917 FBXL13 0.795 0.966 0.044 0 RPL17_SNORD58B 0.917 RBP7 0.848 0.971 0.007 0.007 RPL17_SNORD58B 0.917 CALM2_C2orf61 0.749 0.998 0.013 0 IRS2 0.817 GLT25D1 0.77 0.925 0.02 0.007 IRS2 0.817 ZNF28 0.837 0.915 0.046 0.007 IRS2 0.817 TYMS 0.863 0.933 0.007 0.04 IRS2 0.817 HIST1H2BM 0.891 0.947 0.008 0.039 IRS2 0.817 ABCG1 0.85 0.962 0.001 0.017 IRS2 0.817 HIST1H3B 0.89 0.958 0.01 0.002 IRS2 0.817 HIST1H3H 0.861 0.937 0.039 0.001 IRS2 0.817 APOLD1 0.871 0.949 0.012 0.004 IRS2 0.817 B4GALT3 0.917 0.963 0.005 0.025 IRS2 0.817 CDC26 0.799 0.919 0.033 0.002 GALNT2 0.528 PDK4 0.863 0.911 0 0.002 HSP90AB1_HSP90A 0.924 ACPL2 0.906 0.979 0.019 0.019 HSP90AB1_HSP90A 0.924 CCDC125 0.917 0.994 0.008 0.009 HSP90AB1_HSP90A 0.924 PDK4 0.863 0.993 0.016 0.001 HSP90AB1_HSP90A 0.924 ZRANB1 0.897 0.985 0.009 0.004 HSP90AB1_HSP90A 0.924 MPZL3 0.892 0.979 0.009 0.015 HSP90AB1_HSP90A 0.924 PLIN2 0.706 0.987 0.007 0 HSP90AB1_HSP90A 0.924 RGS2 0.915 0.973 0.006 0.046 GLT25D1 0.77 OR9A2 0.823 0.909 0.011 0.016 GLT25D1 0.77 KCNMA1 0.707 0.867 0.04 0.002 GLT25D1 0.77 PDK4 0.863 0.96 0.004 0 GLT25D1 0.77 THBS1 0.849 0.918 0.026 0.002 GLT25D1 0.77 LHFP 0.731 0.839 0.044 0.021 GLT25D1 0.77 CPM 0.854 0.92 0.022 0.014 GLT25D1 0.77 ATP6V0D1_LOC100 0.567 0.851 0.006 0 GLT25D1 0.77 HIST2H2BF_HIST2 0.855 0.925 0.009 0.018 GLT25D1 0.77 SPATA6 0.812 0.916 0.025 0.004 OR9A2 0.823 EIF1AX_SCARNA9L 0.871 0.933 0.006 0.039 OR9A2 0.823 ZNF28 0.837 0.904 0.025 0.016 OR9A2 0.823 HIST1H3C 0.863 0.918 0.041 0.018 OR9A2 0.823 HIST1H3H 0.861 0.923 0.018 0.007 OR9A2 0.823 S100B 0.841 0.919 0.019 0.033 OR9A2 0.823 IGK@_IGKC_IGKV 0.855 0.914 0.009 0.046 OR9A2 0.823 C7orf58 0.831 0.942 0.011 0.001 OR9A2 0.823 C4orf3 0.723 0.9 0.033 0.002 OR9A2 0.823 CDC26 0.799 0.901 0.004 0.025 OR9A2 0.823 TAS2R31 0.833 0.919 0.011 0.01 OR9A2 0.823 GLDC 0.714 0.882 0.045 0 ACPL2 0.906 CD300A 0.841 0.964 0.028 0.001 ACPL2 0.906 MGST3 0.857 0.964 0.045 0.003 ACPL2 0.906 TYMS 0.863 0.965 0.013 0.01 ACPL2 0.906 RRP12_LOC644215 0.904 0.976 0.01 0.035 ACPL2 0.906 HIST1H2BM 0.891 0.975 0.012 0.023 ACPL2 0.906 ABCG1 0.85 0.979 0.007 0.005 ACPL2 0.906 HIST1H3B 0.89 0.985 0.011 0.006 ACPL2 0.906 FSD1L_GARNL1 0.779 0.956 0.012 0.001 ACPL2 0.906 APOLD1 0.871 0.966 0.036 0.007 ACPL2 0.906 SON 0.856 0.973 0.044 0.003 ACPL2 0.906 IRF1 0.82 0.979 0.014 0.001 ACPL2 0.906 B4GALT3 0.917 0.988 0.009 0.035 ACPL2 0.906 TNFRSF17 0.875 0.978 0.021 0.004 ACPL2 0.906 CDC26 0.799 0.983 0.01 0 LRRFIP1 0.678 ITGA4_CERKL 0.88 0.955 0 0.033 LRRFIP1 0.678 SFRS9 0.558 0.746 0.047 0.033 LRRFIP1 0.678 NPCDR1 0.726 0.817 0.025 0.023 LRRFIP1 0.678 P4HA1_RPL17 0.664 0.857 0.005 0 LRRFIP1 0.678 KLHL5 0.657 0.799 0.019 0.032 LRRFIP1 0.678 KIAA0746 0.776 0.9 0 0.004 LRRFIP1 0.678 E2F6 0.787 0.85 0.004 0.028 LRRFIP1 0.678 PYHIN1 0.686 0.803 0.039 0.016 LRRFIP1 0.678 C7orf58 0.831 0.925 0 0.002 LRRFIP1 0.678 TMEM62_SPCS2_L 0.78 0.884 0 0.048 LRRFIP1 0.678 TAS2R31 0.833 0.9 0 0.046 LRRFIP1 0.678 SDHC 0.736 0.822 0.022 0.023 LRRFIP1 0.678 NFXL1 0.728 0.834 0.004 0.029 KCNMA1 0.707 KIAA0746 0.776 0.843 0.006 0.033 KCNMA1 0.707 HIST1H3C 0.863 0.897 0.002 0.032 KCNMA1 0.707 E2F6 0.787 0.839 0.027 0.023 KCNMA1 0.707 CD151 0.718 0.832 0.027 0.006 KCNMA1 0.707 HIST1H3H 0.861 0.912 0.001 0.013 KCNMA1 0.707 C7orf58 0.831 0.909 0 0.023 KCNMA1 0.707 NP 0.726 0.809 0.03 0.049 KCNMA1 0.707 C4orf3 0.723 0.832 0.011 0.018 KCNMA1 0.707 IGLV6-57 0.761 0.868 0.003 0.005 KCNMA1 0.707 NFXL1 0.728 0.805 0.038 0.029 KCNMA1 0.707 GLDC 0.714 0.825 0.024 0.007 KCNMA1 0.707 DCTN5 0.722 0.792 0.042 0.047 OCR1 0.896 ITGA4_CERKL 0.88 0.964 0.006 0.044 OCR1 0.896 CCR4 0.871 0.978 0.029 0.004 OCR1 0.896 ABCG1 0.85 0.949 0.021 0.012 OCR1 0.896 IGK@_IGKC_IGKV 0.855 0.957 0.049 0.005 OCR1 0.896 TNFRSF17 0.875 0.967 0.023 0.01 OCR1 0.896 CDC26 0.799 0.932 0.042 0.009 ITGA4_CERKL 0.88 KIAA1257_ACAD9/ 0.851 0.944 0.029 0.007 ITGA4_CERKL 0.88 CCDC125 0.917 0.995 0.002 0.007 ITGA4_CERKL 0.88 PDK4 0.863 0.985 0.006 0.001 ITGA4_CERKL 0.88 ZRANB1 0.897 0.983 0.005 0.002 ITGA4_CERKL 0.88 ZNF587_ZNF417 0.632 0.957 0.025 0 ITGA4_CERKL 0.88 CYP4F3_CYP4F2 0.901 0.965 0.05 0.014 ITGA4_CERKL 0.88 SYNE2 0.748 0.986 0.005 0 ITGA4_CERKL 0.88 MME 0.894 0.965 0.038 0.006 ITGA4_CERKL 0.88 PLIN2 0.706 0.953 0.024 0 ITGA4_CERKL 0.88 RBP7 0.848 0.94 0.036 0.012 ITGA4_CERKL 0.88 SPATA6 0.812 0.942 0.038 0.006 EIF1AX_SCARNA9L 0.871 DPH3 0.749 0.928 0.019 0.001 EIF1AX_SCARNA9L 0.871 KIAA1257_ACAD9/ 0.851 0.935 0.004 0.034 EIF1AX_SCARNA9L 0.871 PDK4 0.863 0.943 0.006 0.026 EIF1AX_SCARNA9L 0.871 THBS1 0.849 0.923 0.022 0.032 EIF1AX_SCARNA9L 0.871 MYL9 0.641 0.908 0.036 0 EIF1AX_SCARNA9L 0.871 SYNE2 0.748 0.934 0.006 0.001 EIF1AX_SCARNA9L 0.871 MME 0.894 0.96 0.009 0.028 EIF1AX_SCARNA9L 0.871 RBP7 0.848 0.951 0.008 0.013 EIF1AX_SCARNA9L 0.871 PLEKHF2 0.667 0.93 0.044 0 EIF1AX_SCARNA9L 0.871 CALM2_C2orf61 0.749 0.935 0.019 0 EIF1AX_SCARNA9L 0.871 SPATA6 0.812 0.926 0.013 0.019 DPH3 0.749 ZNF28 0.837 0.932 0 0.026 DPH3 0.749 CCR4 0.871 0.913 0.001 0.048 DPH3 0.749 HIST1H3B 0.89 0.957 0 0.034 DPH3 0.749 HIST1H3C 0.863 0.921 0.002 0.014 DPH3 0.749 HIST1H3H 0.861 0.947 0 0.01 DPH3 0.749 S100B 0.841 0.9 0.011 0.03 DPH3 0.749 NP 0.726 0.84 0.023 0.042 DPH3 0.749 TAS2R31 0.833 0.909 0.001 0.015 DPH3 0.749 KIAA0101_CSNK1G 0.736 0.869 0.02 0.001 ERGIC1 0.886 CD300A 0.841 0.969 0.018 0.001 ERGIC1 0.886 TYMS 0.863 0.954 0.028 0.009 ERGIC1 0.886 RRP12_LOC644215 0.904 0.981 0.013 0.012 ERGIC1 0.886 ABCG1 0.85 0.974 0.016 0.005 ERGIC1 0.886 IRF1 0.82 0.967 0.011 0.001 ERGIC1 0.886 B4GALT3 0.917 0.976 0.015 0.044 ERGIC1 0.886 CDC26 0.799 0.969 0.04 0 CD300A 0.841 HAL 0.942 0.992 0 0.019 CD300A 0.841 KIAA1257_ACAD9/ 0.851 0.941 0.019 0.002 CD300A 0.841 CCDC125 0.917 0.969 0.001 0.018 CD300A 0.841 PDK4 0.863 0.941 0.028 0.001 CD300A 0.841 ZRANB1 0.897 0.945 0.01 0.01 CD300A 0.841 MPZL3 0.892 0.972 0 0.018 CD300A 0.841 KDM6B_TMEM88 0.869 0.967 0.004 0.014 CD300A 0.841 CYP4F3_CYP4F2 0.901 0.948 0.011 0.046 CD300A 0.841 MME 0.894 0.944 0.015 0.023 CD300A 0.841 RBP7 0.848 0.928 0.026 0.005 CD300A 0.841 HIST2H2BF_HIST2 0.855 0.962 0.015 0.001 NF-E4 0.861 ZNF28 0.837 0.949 0.01 0.006 NF-E4 0.861 CCR4 0.871 0.938 0.031 0.04 NF-E4 0.861 ABCG1 0.85 0.953 0.004 0.013 NF-E4 0.861 HIST1H3B 0.89 0.957 0.004 0.042 NF-E4 0.861 HIST1H3H 0.861 0.938 0.037 0.004 NF-E4 0.861 IRF1 0.82 0.929 0.001 0.044 NF-E4 0.861 C7orf58 0.831 0.931 0.047 0.005 NF-E4 0.861 TNFRSF17 0.875 0.941 0.019 0.04 NF-E4 0.861 CDC26 0.799 0.938 0.001 0.012 NF-E4 0.861 TAS2R31 0.833 0.939 0.039 0.002 NF-E4 0.861 DCTN5 0.722 0.91 0.039 0.001 MINPP1 0.7 DLEU2_DLEU2L 0.711 0.829 0.002 0.047 ZNF28 0.837 DLEU2_DLEU2L 0.711 0.935 0.02 0 ZNF28 0.837 KIAA1257_ACAD9/ 0.851 0.923 0.011 0.045 ZNF28 0.837 PDK4 0.863 0.939 0.009 0.012 ZNF28 0.837 THBS1 0.849 0.918 0.027 0.016 ZNF28 0.837 LHFP 0.731 0.887 0.029 0.004 ZNF28 0.837 ZRANB1 0.897 0.951 0.004 0.037 ZNF28 0.837 SYNE2 0.748 0.91 0.043 0.001 ZNF28 0.837 MME 0.894 0.956 0.004 0.036 ZNF28 0.837 CPM 0.854 0.916 0.027 0.047 ZNF28 0.837 RBP7 0.848 0.957 0.001 0.008 ZNF28 0.837 CALM2_C2orf61 0.749 0.934 0.017 0 ZNF28 0.837 SPATA6 0.812 0.922 0.007 0.02 NPCDR1 0.726 ZNF587_ZNF417 0.632 0.82 0.004 0.007 NA 0.682 PYHIN1 0.686 0.805 0.025 0.036 NA 0.682 SDHC 0.736 0.821 0.027 0.015 NA 0.682 KIAA0101_CSNK1G 0.736 0.833 0.005 0.032 ICAM1 0.675 ATP6V0D1_LOC100 0.567 0.788 0.003 0.01 P4HA1_RPL17 0.664 DLEU2_DLEU2L 0.711 0.822 0.002 0.039 P4HA1_RPL17 0.664 PDK4 0.863 0.953 0 0.014 P4HA1_RPL17 0.664 ZNF587_ZNF417 0.632 0.816 0.002 0.005 P4HA1_RPL17 0.664 PLEKHF2 0.667 0.816 0.003 0.021 P4HA1_RPL17 0.664 IL1RL1 0.738 0.834 0.004 0.03 C15orf54 0.72 DLEU2_DLEU2L 0.711 0.819 0.015 0.034 KLHL5 0.657 ZNF587_ZNF417 0.632 0.787 0.006 0.016 HAL 0.942 ABCG1 0.85 0.99 0.018 0.002 HAL 0.942 TIMM10 0.949 0.986 0.03 0.028 HAL 0.942 IRF1 0.82 0.986 0.016 0 HAL 0.942 LASS4 0.924 0.982 0.01 0.045 HAL 0.942 B4GALT3 0.917 0.989 0.019 0.02 HAL 0.942 CDC26 0.799 0.99 0.02 0 DLEU2_DLEU2L 0.711 KIAA0746 0.776 0.85 0.007 0.046 DLEU2_DLEU2L 0.711 FSD1L_GARNL1 0.779 0.879 0 0.043 DLEU2_DLEU2L 0.711 NA 0.7 0.865 0.023 0 DLEU2_DLEU2L 0.711 HIST1H3C 0.863 0.918 0.001 0.035 DLEU2_DLEU2L 0.711 E2F6 0.787 0.856 0.01 0.014 DLEU2_DLEU2L 0.711 HIST1H3H 0.861 0.934 0 0.023 DLEU2_DLEU2L 0.711 C7orf58 0.831 0.9 0.001 0.017 DLEU2_DLEU2L 0.711 NP 0.726 0.851 0.004 0.024 DLEU2_DLEU2L 0.711 PLEKHA3 0.645 0.868 0.002 0 DLEU2_DLEU2L 0.711 TMEM62_SPCS2_L 0.78 0.865 0.006 0.049 DLEU2_DLEU2L 0.711 SDHC 0.736 0.869 0.015 0 DLEU2_DLEU2L 0.711 NFXL1 0.728 0.825 0.019 0.021 DLEU2_DLEU2L 0.711 KIAA0101_CSNK1G 0.736 0.863 0.003 0.005 KIAA1257_ACAD9/ 0.851 ABCG1 0.85 0.963 0.001 0.01 KIAA1257_ACAD9/ 0.851 HIST1H3B 0.89 0.943 0.017 0.048 KIAA1257_ACAD9/ 0.851 IGK@_IGKC_IGKV 0.855 0.924 0.045 0.014 KIAA1257_ACAD9/ 0.851 SON 0.856 0.936 0.045 0.014 KIAA1257_ACAD9/ 0.851 IRF1 0.82 0.941 0.001 0.016 KIAA1257_ACAD9/ 0.851 TNFRSF17 0.875 0.944 0.015 0.013 KIAA1257_ACAD9/ 0.851 CDC26 0.799 0.941 0.003 0.004 MGST3 0.857 CCDC125 0.917 0.963 0.003 0.048 MGST3 0.857 PDK4 0.863 0.974 0.006 0.001 MGST3 0.857 ZRANB1 0.897 0.952 0.007 0.021 MGST3 0.857 KDM6B_TMEM88 0.869 0.953 0.01 0.027 MGST3 0.857 CYP4F3_CYP4F2 0.901 0.951 0.032 0.027 MGST3 0.857 RBP7 0.848 0.956 0.005 0.004 KIAA0746 0.776 PDK4 0.863 0.963 0 0.007 KIAA0746 0.776 ZNF587_ZNF417 0.632 0.893 0.001 0 KIAA0746 0.776 SYNE2 0.748 0.911 0.002 0.004 KIAA0746 0.776 PLIN2 0.706 0.892 0.001 0.008 KIAA0746 0.776 PLEKHF2 0.667 0.871 0.014 0.001 KIAA0746 0.776 IL1RL1 0.738 0.88 0.034 0.003 KIAA0746 0.776 MPZL2 0.699 0.867 0.023 0.004 KIAA0746 0.776 LOC100128751 0.686 0.864 0.02 0.006 KIAA0746 0.776 PPP1R2_PPP1R2P3 0.702 0.903 0.002 0.001 HSPB1_HSPBL2 0.705 PDK4 0.863 0.905 0.001 0.03 CCR4 0.871 PDK4 0.863 0.966 0.012 0.004 CCR4 0.871 ZRANB1 0.897 0.97 0.003 0.027 CCR4 0.871 ZNF587_ZNF417 0.632 0.931 0.033 0 CCR4 0.871 SYNE2 0.748 0.948 0.013 0 CCR4 0.871 MME 0.894 0.967 0.009 0.037 CCR4 0.871 RBP7 0.848 0.929 0.018 0.047 CCR4 0.871 PPP1R2_PPP1R2P3 0.702 0.939 0.035 0 TYMS 0.863 ZRANB1 0.897 0.961 0.005 0.01 TYMS 0.863 CYP4F3_CYP4F2 0.901 0.947 0.049 0.031 TYMS 0.863 MME 0.894 0.954 0.026 0.02 TYMS 0.863 CPM 0.854 0.934 0.049 0.013 TYMS 0.863 RBP7 0.848 0.973 0.007 0 RRP12_LOC644215 0.904 CCDC125 0.917 0.967 0.019 0.028 RRP12_LOC644215 0.904 MPZL3 0.892 0.983 0.005 0.009 RRP12_LOC644215 0.904 KDM6B_TMEM88 0.869 0.976 0.017 0.015 RRP12_LOC644215 0.904 RGS2 0.915 0.971 0.011 0.044 CCDC125 0.917 ABCG1 0.85 0.987 0.009 0.001 CCDC125 0.917 FAM118A 0.882 0.974 0.03 0.008 CCDC125 0.917 IRF1 0.82 0.987 0.007 0 CCDC125 0.917 B4GALT3 0.917 0.99 0.008 0.016 CCDC125 0.917 TNFRSF17 0.875 0.981 0.032 0.002 CCDC125 0.917 CDC26 0.799 0.959 0.034 0 CCDC125 0.917 DCTN5 0.722 0.969 0.039 0 HIST1H2BM 0.891 ZRANB1 0.897 0.976 0.014 0.006 HIST1H2BM 0.891 MME 0.894 0.963 0.03 0.027 HIST1H2BM 0.891 RBP7 0.848 0.966 0.03 0.001 PDK4 0.863 LAIR1_LAIR2 0.607 0.932 0.033 0 PDK4 0.863 HIST1H3B 0.89 0.946 0.015 0.039 PDK4 0.863 FSD1L_GARNL1 0.779 0.941 0.002 0.003 PDK4 0.863 CD151 0.718 0.932 0.036 0 PDK4 0.863 IGK@_IGKC_IGKV 0.855 0.955 0.009 0.006 PDK4 0.863 SON 0.856 0.948 0.008 0.018 PDK4 0.863 IRF1 0.82 0.941 0.001 0.018 PDK4 0.863 C7orf58 0.831 0.97 0.009 0 PDK4 0.863 CTSL1_CTSLL3 0.676 0.929 0.038 0 PDK4 0.863 NP 0.726 0.947 0.014 0 PDK4 0.863 TNFRSF17 0.875 0.938 0.018 0.049 PDK4 0.863 TMEM62_SPCS2_L 0.78 0.966 0.015 0 PDK4 0.863 CDC26 0.799 0.942 0.004 0.002 PDK4 0.863 TAS2R31 0.833 0.952 0.016 0.001 PDK4 0.863 NFXL1 0.728 0.946 0.039 0 PDK4 0.863 DCTN5 0.722 0.955 0.004 0 ABCG1 0.85 ZRANB1 0.897 0.944 0.018 0.019 ABCG1 0.85 CYP4F3_CYP4F2 0.901 0.965 0.008 0.026 ABCG1 0.85 MME 0.894 0.969 0.006 0.013 ABCG1 0.85 RBP7 0.848 0.963 0.009 0.001 THBS1 0.849 HIST1H3B 0.89 0.96 0.002 0.028 THBS1 0.849 FSD1L_GARNL1 0.779 0.909 0.008 0.015 THBS1 0.849 PTGS1 0.675 0.93 0.012 0 THBS1 0.849 HIST1H3C 0.863 0.936 0.023 0.011 THBS1 0.849 HIST1H3H 0.861 0.941 0.011 0.012 THBS1 0.849 IGK@_IGKC_IGKV 0.855 0.928 0.021 0.028 THBS1 0.849 SON 0.856 0.926 0.031 0.042 THBS1 0.849 C4orf3 0.723 0.911 0.027 0.001 THBS1 0.849 CDC26 0.799 0.919 0.005 0.015 THBS1 0.849 DCTN5 0.722 0.894 0.03 0.002 ITGA2B 0.58 MYL9 0.641 0.819 0 0.023 LHFP 0.731 HIST1H3B 0.89 0.929 0 0.02 LHFP 0.731 HIST1H3C 0.863 0.903 0.005 0.009 LHFP 0.731 HIST1H3H 0.861 0.907 0.003 0.012 LHFP 0.731 APOLD1 0.871 0.937 0 0.009 LHFP 0.731 IGLV6-57 0.761 0.864 0.036 0 LAIR1_LAIR2 0.607 PLIN2 0.706 0.827 0.002 0.037 HIST1H3B 0.89 ZRANB1 0.897 0.959 0.024 0.037 HIST1H3B 0.89 MYL9 0.641 0.948 0.029 0 HIST1H3B 0.89 CPM 0.854 0.946 0.026 0.018 HIST1H3B 0.89 RBP7 0.848 0.97 0.003 0.004 HIST1H3B 0.89 CALM2_C2orf61 0.749 0.959 0.022 0 HIST1H3B 0.89 SPATA6 0.812 0.939 0.043 0.011 ZRANB1 0.897 FAM118A 0.882 0.975 0.012 0.007 ZRANB1 0.897 IRF1 0.82 0.954 0.011 0.004 ZRANB1 0.897 B4GALT3 0.917 0.982 0.002 0.046 ZRANB1 0.897 NP 0.726 0.955 0.036 0 ZRANB1 0.897 TNFRSF17 0.875 0.959 0.028 0.006 ZRANB1 0.897 CDC26 0.799 0.95 0.016 0.001 TIMM10 0.949 MPZL3 0.892 0.983 0.03 0.006 TIMM10 0.949 RBP7 0.848 0.985 0.045 0.001 TIMM10 0.949 RGS2 0.915 0.981 0.04 0.008 FSD1L_GARNL1 0.779 CPM 0.854 0.909 0.014 0.033 FSD1L_GARNL1 0.779 HIST2H2BF_HIST2 0.855 0.917 0.005 0.041 FSD1L_GARNL1 0.779 SPATA6 0.812 0.913 0.013 0.004 HIST1H2AJ_HIST1 0.77 NA 0.627 0.824 0.021 0.005 PTGS1 0.675 MYL9 0.641 0.933 0 0 PTGS1 0.675 SPARC 0.574 0.854 0.018 0 NA 0.7 ZNF587_ZNF417 0.632 0.771 0.017 0.046 NA 0.7 PLEKHF2 0.667 0.873 0 0.005 HIST1H3C 0.863 MYL9 0.641 0.918 0.005 0 HIST1H3C 0.863 CALM2_C2orf61 0.749 0.922 0.015 0.004 E2F6 0.787 ZNF587_ZNF417 0.632 0.838 0.024 0.002 E2F6 0.787 IL1RL1 0.738 0.859 0.016 0.019 E2F6 0.787 MPZL2 0.699 0.844 0.021 0.015 E2F6 0.787 CALM2_C2orf61 0.749 0.896 0 0.013 MPZL3 0.892 LASS4 0.924 0.987 0.004 0.015 MPZL3 0.892 B4GALT3 0.917 0.96 0.044 0.019 SRXN1 0.513 HIST1H3H 0.861 0.918 0 0.013 SRXN1 0.513 POLE2 0.694 0.743 0.013 0.035 CD151 0.718 ATP6V0D1_LOC100 0.567 0.77 0.048 0.024 HIST1H3H 0.861 MYL9 0.641 0.944 0.002 0 HIST1H3H 0.861 RBP7 0.848 0.948 0.001 0.029 HIST1H3H 0.861 CALM2_C2orf61 0.749 0.93 0.015 0.001 HIST1H3H 0.861 SPATA6 0.812 0.923 0.002 0.045 FSD1L 0.674 IL1RL1 0.738 0.82 0.016 0.041 S100B 0.841 SYNE2 0.748 0.898 0.035 0.014 S100B 0.841 LOC100128751 0.686 0.898 0.04 0 S100B 0.841 CALM2_C2orf61 0.749 0.895 0.026 0.017 ZNF587_ZNF417 0.632 PYHIN1 0.686 0.804 0.015 0.009 ZNF587_ZNF417 0.632 IGK@_IGKC_IGKV 0.855 0.915 0 0.027 ZNF587_ZNF417 0.632 TSHZ2 0.733 0.819 0.007 0.021 ZNF587_ZNF417 0.632 TMEM62_SPCS2_L 0.78 0.873 0 0.018 ZNF587_ZNF417 0.632 NFXL1 0.728 0.841 0.001 0.005 ZNF587_ZNF417 0.632 DCTN5 0.722 0.875 0 0.006 PYHIN1 0.686 SYNE2 0.748 0.956 0 0 PYHIN1 0.686 PPP1R2_PPP1R2P3 0.702 0.847 0.001 0.03 APOLD1 0.871 RBP7 0.848 0.948 0.033 0.001 FABP2 0.744 TAS2R31 0.833 0.88 0.01 0.05 FABP2 0.744 IGLV6-57 0.761 0.866 0.018 0.004 KDM6B_TMEM88 0.869 IRF1 0.82 0.956 0.03 0.001 IGK@_IGKC_IGKV 0.855 SYNE2 0.748 0.923 0.036 0 IGK@_IGKC_IGKV 0.855 MME 0.894 0.944 0.018 0.046 IGK@_IGKC_IGKV 0.855 PLIN2 0.706 0.911 0.039 0.001 IGK@_IGKC_IGKV 0.855 LOC100128751 0.686 0.916 0.044 0 IGK@_IGKC_IGKV 0.855 HIST2H2BF_HIST2 0.855 0.934 0.029 0.017 MYL9 0.641 HIST1H2BJ 0.703 0.815 0.02 0.005 MYL9 0.641 CMTM5 0.671 0.872 0.001 0.001 MYL9 0.641 SDHC 0.736 0.793 0.039 0.031 MYL9 0.641 KIAA0101_CSNK1G 0.736 0.82 0.01 0.008 CLC 0.782 PLIN2 0.706 0.901 0.003 0.013 CLC 0.782 IL1RL1 0.738 0.93 0.015 0 CYP4F3_CYP4F2 0.901 IRF1 0.82 0.96 0.019 0.009 CYP4F3_CYP4F2 0.901 CDC26 0.799 0.952 0.026 0.004 SON 0.856 SYNE2 0.748 0.938 0.009 0.001 SON 0.856 RBP7 0.848 0.966 0.007 0.006 IRF1 0.82 MME 0.894 0.947 0.017 0.008 IRF1 0.82 RBP7 0.848 0.944 0.007 0.002 IRF1 0.82 RGS2 0.915 0.975 0 0.03 IRF1 0.82 HIST2H2BF_HIST2 0.855 0.951 0.019 0.001 SYNE2 0.748 C7orf58 0.831 0.912 0.003 0.017 SYNE2 0.748 NP 0.726 0.883 0.008 0.004 SYNE2 0.748 TMEM62_SPCS2_L 0.78 0.919 0.002 0 SYNE2 0.748 TAS2R31 0.833 0.938 0.001 0 SYNE2 0.748 NFXL1 0.728 0.881 0.009 0.001 SYNE2 0.748 DCTN5 0.722 0.893 0.002 0.004 MME 0.894 TNFRSF17 0.875 0.96 0.007 0.029 MME 0.894 CDC26 0.799 0.939 0.014 0.007 C7orf58 0.831 FBXL13 0.795 0.897 0.045 0.019 C7orf58 0.831 PLEKHF2 0.667 0.895 0.046 0 C7orf58 0.831 IL1RL1 0.738 0.898 0.042 0.003 C7orf58 0.831 AIF1 0.618 0.918 0.004 0 C7orf58 0.831 PPP1R2_PPP1R2P3 0.702 0.898 0.016 0.002 C7orf58 0.831 HIST2H2BF_HIST2 0.855 0.961 0 0.022 C7orf58 0.831 SPATA6 0.812 0.92 0.001 0.049 CPM 0.854 CDC26 0.799 0.931 0.01 0.01 TSHZ2 0.733 LOC100128751 0.686 0.824 0.036 0.042 TSHZ2 0.733 PPP1R2_PPP1R2P3 0.702 0.83 0.045 0.044 PLIN2 0.706 NP 0.726 0.836 0.009 0.04 PLIN2 0.706 TMEM62_SPCS2_L 0.78 0.918 0.003 0.003 PLIN2 0.706 DCTN5 0.722 0.861 0.005 0.011 B4GALT3 0.917 RBP7 0.848 0.981 0.045 0.001 B4GALT3 0.917 RGS2 0.915 0.987 0.009 0.012 NP 0.726 FBXL13 0.795 0.857 0.009 0.05 TNFRSF17 0.875 RBP7 0.848 0.934 0.013 0.048 FBXL13 0.795 TAS2R31 0.833 0.92 0.003 0.019 FBXL13 0.795 DCTN5 0.722 0.859 0.032 0.018 PLEKHA3 0.645 PLEKHF2 0.667 0.781 0.045 0.029 TMEM62_SPCS2_L 0.78 PLEKHF2 0.667 0.859 0.042 0.002 TMEM62_SPCS2_L 0.78 PPP1R2_PPP1R2P3 0.702 0.877 0.015 0.002 TMEM62_SPCS2_L 0.78 CALM2_C2orf61 0.749 0.889 0.02 0.014 RBP7 0.848 CDC26 0.799 0.964 0.001 0.001 RBP7 0.848 TAS2R31 0.833 0.936 0.046 0.001 PLEKHF2 0.667 SDHC 0.736 0.842 0.021 0.001 PLEKHF2 0.667 NFXL1 0.728 0.814 0.012 0.027 RGS2 0.915 CDC26 0.799 0.975 0.019 0 IL1RL1 0.738 IL5RA 0.782 0.975 0 0.002 IL1RL1 0.738 TAS2R31 0.833 0.928 0 0.019 IL1RL1 0.738 KIAA0101_CSNK1G 0.736 0.829 0.029 0.03 AIF1 0.618 SDHC 0.736 0.801 0.023 0.017 MPZL2 0.699 NFXL1 0.728 0.814 0.034 0.029 MPZL2 0.699 DCTN5 0.722 0.845 0.002 0.028 CDC26 0.799 HIST2H2BF_HIST2 0.855 0.94 0.012 0.001 CDC26 0.799 SPATA6 0.812 0.923 0.012 0.004 PPP1R2_PPP1R2P3 0.702 NFXL1 0.728 0.835 0.03 0.009 TAS2R31 0.833 HIST2H2BF_HIST2 0.855 0.926 0.012 0.019 TAS2R31 0.833 CALM2_C2orf61 0.749 0.896 0.037 0.003 CALM2_C2orf61 0.749 IGLV6-57 0.761 0.852 0.01 0.034 CALM2_C2orf61 0.749 SDHC 0.736 0.879 0.05 0 CALM2_C2orf61 0.749 KIAA0101_CSNK1G 0.736 0.903 0.003 0

TABLE 17 Ratios Healthy Versus ipSIRS Ratio Signif Ratio Signif Gene 1 Name Gene 1 AUC Gene 2 Name Gene 2 AUC Ratio AUC to Gene 1 to Gene 2 VNN1 0.933 GNLY 0.917 0.986 0.042 0.019 VNN1 0.933 PTGDR 0.919 0.991 0.043 0.013 VNN1 0.933 SH2D1B 0.867 0.986 0.046 0.005 VNN1 0.933 HIST1H4C 0.859 0.976 0.041 0.008 VNN1 0.933 VAMP2 0.673 0.963 0.029 0 VNN1 0.933 MRPL41 0.811 0.985 0.039 0 VNN1 0.933 CASS4 0.877 0.981 0.037 0.006 VNN1 0.933 ITK 0.899 0.991 0.034 0.004 VNN1 0.933 B3GAT3 0.783 0.975 0.05 0 VNN1 0.933 HIST1H4E 0.83 0.978 0.027 0.002 VNN1 0.933 CHI3L1 0.909 0.985 0.043 0.047 VNN1 0.933 RUNX2 0.848 0.982 0.023 0.004 VNN1 0.933 DNAJC9_FAM149B1 0.811 0.99 0.048 0 VNN1 0.933 PDE3B 0.85 0.978 0.031 0.005 VNN1 0.933 IRF4 0.781 0.97 0.031 0.001 VNN1 0.933 LY6G5B_CSNK2B 0.661 0.979 0.029 0 VNN1 0.933 KIAA0746 0.544 0.985 0.044 0 VNN1 0.933 CCR4 0.784 0.972 0.05 0 VNN1 0.933 MME 0.507 0.98 0.05 0 VNN1 0.933 TSHZ2 0.801 0.973 0.035 0.001 VNN1 0.933 RASA4_RASA4P_R 0.779 0.969 0.045 0 VNN1 0.933 RGS2 0.605 0.975 0.034 0 IMP3 0.942 OMG 0.96 0.997 0.019 0.031 IMP3 0.942 SLC37A3 0.885 0.99 0.05 0.007 IMP3 0.942 IL18R1 0.933 0.993 0.026 0.013 IMP3 0.942 ERLIN1 0.937 0.989 0.04 0.023 IMP3 0.942 C7orf53 0.921 0.99 0.024 0.008 IMP3 0.942 DSE 0.936 0.994 0.02 0.038 IMP3 0.942 DNAJC13 0.948 0.998 0.021 0.038 IMP3 0.942 GSR 0.831 0.981 0.045 0 IMP3 0.942 B3GNT5_MCF2L2 0.956 0.997 0.018 0.026 IMP3 0.942 PICALM 0.879 0.992 0.021 0.003 IMP3 0.942 METTL7B 0.943 0.99 0.022 0.035 IMP3 0.942 IFI16 0.912 0.984 0.033 0.011 IMP3 0.942 JKAMP 0.93 1 0.021 0.008 IMP3 0.942 WSB2 0.911 0.995 0.024 0.003 IMP3 0.942 CLU 0.894 0.979 0.041 0.006 IMP3 0.942 CD63 0.757 0.999 0.022 0 IMP3 0.942 HPSE 0.939 0.997 0.023 0.007 IMP3 0.942 DDAH2 0.886 0.984 0.016 0.017 IMP3 0.942 ATP13A3 0.841 0.985 0.039 0.004 IMP3 0.942 LRRC70_IPO11 0.923 0.995 0.023 0.009 IMP3 0.942 CDS2 0.869 0.982 0.022 0.007 IMP3 0.942 OLAH 0.885 0.981 0.036 0.008 IMP3 0.942 EAF2_HCG11_LOC 0.917 0.986 0.032 0.029 IMP3 0.942 EXOSC4 0.731 0.977 0.031 0 IMP3 0.942 FOLR3_FOLR2 0.848 0.992 0.046 0 IMP3 0.942 LGALS1 0.736 0.99 0.028 0 IMP3 0.942 SIAE 0.814 0.984 0.03 0 IMP3 0.942 EFCAB2 0.96 0.989 0.04 0.03 IMP3 0.942 AGTRAP 0.856 0.989 0.025 0.001 IMP3 0.942 SUCNR1 0.922 0.996 0.029 0.005 IMP3 0.942 AIG1 0.842 0.986 0.042 0.001 IMP3 0.942 HS2ST1_UBA2 0.758 0.977 0.035 0 IMP3 0.942 VOPP1_LOC100128 0.618 0.98 0.037 0 IMP3 0.942 CLEC4A 0.87 0.985 0.044 0.002 IMP3 0.942 TRIM21 0.874 0.989 0.026 0.004 IMP3 0.942 MGST3 0.602 0.988 0.038 0 IMP3 0.942 ATP6V0D1_LOC100 0.856 0.978 0.016 0.008 IMP3 0.942 CALM2_C2orf61 0.733 0.99 0.026 0 CLEC4D 0.976 CASS4 0.877 0.996 0.048 0.001 GPR56 0.933 FAR2 0.963 0.998 0.007 0.042 GPR56 0.933 CLEC4E 0.883 0.982 0.03 0.003 GPR56 0.933 IL18R1 0.933 0.98 0.048 0.014 GPR56 0.933 ERLIN1 0.937 0.996 0.007 0.031 GPR56 0.933 C7orf53 0.921 0.984 0.018 0.012 GPR56 0.933 DSE 0.936 0.972 0.043 0.037 GPR56 0.933 DNAJC13 0.948 0.981 0.034 0.033 GPR56 0.933 FOXD4L3_FOXD4L6 0.892 0.981 0.041 0.004 GPR56 0.933 GSR 0.831 0.977 0.021 0.001 GPR56 0.933 PICALM 0.879 0.968 0.03 0.012 GPR56 0.933 IFI16 0.912 0.983 0.011 0.012 GPR56 0.933 JKAMP 0.93 0.984 0.004 0.043 GPR56 0.933 WSB2 0.911 0.978 0.007 0.019 GPR56 0.933 CLU 0.894 0.975 0.02 0.006 GPR56 0.933 HPSE 0.939 0.986 0.007 0.023 GPR56 0.933 LRRC70_IPO11 0.923 0.975 0.035 0.025 GPR56 0.933 CDS2 0.869 0.97 0.011 0.016 GPR56 0.933 OLAH 0.885 0.976 0.023 0.01 GPR56 0.933 RCBTB2_LOC10013 0.905 0.972 0.038 0.016 GPR56 0.933 SIAE 0.814 0.968 0.049 0 GPR56 0.933 AP3B2 0.887 0.961 0.044 0.028 GPR56 0.933 SUCNR1 0.922 0.976 0.022 0.023 GPR56 0.933 ACTA2 0.783 0.977 0.042 0 GPR56 0.933 KCNMA1 0.897 0.975 0.02 0.03 GPR56 0.933 TRIM21 0.874 0.986 0.011 0.006 GPR56 0.933 ICAM1 0.821 0.971 0.028 0.002 GPR56 0.933 ATP6V0D1_LOC100 0.856 0.968 0.005 0.019 ARG1 0.922 HIST1H4C 0.859 0.968 0.043 0.021 ARG1 0.922 VAMP2 0.673 0.944 0.049 0 ARG1 0.922 MRPL41 0.811 0.978 0.046 0.001 ARG1 0.922 HIST1H4E 0.83 0.963 0.04 0.01 ARG1 0.922 RUNX2 0.848 0.961 0.04 0.026 ARG1 0.922 HIST1H3A 0.715 0.967 0.043 0 ARG1 0.922 IRF4 0.781 0.952 0.05 0.006 ARG1 0.922 IRS2 0.648 0.954 0.048 0 FAR2 0.963 CAMK4 0.953 0.998 0.041 0.04 FAR2 0.963 CASS4 0.877 0.998 0.037 0 FAR2 0.963 RUNX2 0.848 0.996 0.043 0 FAR2 0.963 PDE3B 0.85 0.997 0.041 0 GNLY 0.917 SLC37A3 0.885 0.983 0.02 0.01 GNLY 0.917 CLEC4E 0.883 0.977 0.03 0.003 GNLY 0.917 IL18R1 0.933 0.981 0.018 0.027 GNLY 0.917 ERLIN1 0.937 0.978 0.041 0.01 GNLY 0.917 C7orf53 0.921 0.983 0.01 0.009 GNLY 0.917 DSE 0.936 0.983 0.019 0.017 GNLY 0.917 DNAJC13 0.948 0.986 0.02 0.015 GNLY 0.917 FOXD4L3_FOXD4L6 0.892 0.977 0.042 0.004 GNLY 0.917 B3GNT5_MCF2L2 0.956 0.992 0.004 0.029 GNLY 0.917 PICALM 0.879 0.974 0.015 0.007 GNLY 0.917 TLR10 0.905 0.974 0.023 0.007 GNLY 0.917 IFI16 0.912 0.984 0.008 0.005 GNLY 0.917 JKAMP 0.93 0.993 0.004 0.013 GNLY 0.917 WSB2 0.911 0.987 0.004 0.007 GNLY 0.917 CLU 0.894 0.975 0.01 0.007 GNLY 0.917 HPSE 0.939 0.993 0.006 0.008 GNLY 0.917 LRRC70_IPO11 0.923 0.988 0.007 0.01 GNLY 0.917 CDS2 0.869 0.959 0.009 0.029 GNLY 0.917 OLAH 0.885 0.974 0.008 0.01 GNLY 0.917 EAF2_HCG11_LOC 0.917 0.975 0.046 0.02 GNLY 0.917 RCBTB2_LOC10013 0.905 0.967 0.024 0.016 GNLY 0.917 FOLR3_FOLR2 0.848 0.98 0.014 0.001 GNLY 0.917 SIAE 0.814 0.972 0.023 0 GNLY 0.917 AP3B2 0.887 0.947 0.046 0.048 GNLY 0.917 ABCA13 0.868 0.976 0.032 0.001 GNLY 0.917 CDA 0.804 0.97 0.04 0 GNLY 0.917 SAP30 0.728 0.952 0.011 0 GNLY 0.917 AGTRAP 0.856 0.969 0.004 0.005 GNLY 0.917 SUCNR1 0.922 0.987 0.012 0.008 GNLY 0.917 KCNMA1 0.897 0.964 0.015 0.044 GNLY 0.917 TRIM21 0.874 0.972 0.026 0.007 GNLY 0.917 DLEU2_DLEU2L 0.874 0.966 0.036 0.002 GNLY 0.917 ANKRD28 0.8 0.977 0.039 0 GNLY 0.917 ATP6V0D1_LOC100 0.856 0.953 0.021 0.029 OMG 0.96 PTGDR 0.919 0.998 0.026 0.01 OMG 0.96 CAMK4 0.953 0.997 0.034 0.042 OMG 0.96 LRRN3 0.947 0.998 0.023 0.033 OMG 0.96 PDE3B 0.85 0.997 0.028 0.001 SLC37A3 0.885 PTGDR 0.919 0.979 0.022 0.021 SLC37A3 0.885 VAMP2 0.673 0.941 0.018 0 SLC37A3 0.885 MRPL41 0.811 0.958 0.046 0.001 SLC37A3 0.885 CASS4 0.877 0.972 0.01 0.013 SLC37A3 0.885 ITK 0.899 0.985 0.011 0.004 SLC37A3 0.885 GOT2 0.905 0.975 0.01 0.034 SLC37A3 0.885 B3GAT3 0.783 0.961 0.021 0 SLC37A3 0.885 HIST1H4E 0.83 0.968 0.014 0.002 SLC37A3 0.885 ANAPC11 0.818 0.954 0.035 0.002 SLC37A3 0.885 RUNX2 0.848 0.969 0.01 0.007 SLC37A3 0.885 PMS2CL_PMS2 0.685 0.948 0.043 0 SLC37A3 0.885 PDE3B 0.85 0.975 0.008 0.007 SLC37A3 0.885 SFRS9 0.527 0.942 0.027 0 SLC37A3 0.885 NPCDR1 0.86 0.954 0.035 0.006 SLC37A3 0.885 LY6G5B_CSNK2B 0.661 0.965 0.014 0 SLC37A3 0.885 CCR4 0.784 0.945 0.043 0.001 SLC37A3 0.885 LASS4 0.794 0.951 0.021 0.009 SLC37A3 0.885 TSHZ2 0.801 0.955 0.023 0.002 SLC37A3 0.885 CAMK1D 0.702 0.949 0.03 0 BMX_HNRPDL 0.947 GOT2 0.905 0.988 0.05 0.018 BMX_HNRPDL 0.947 ABCG1 0.789 0.965 0.045 0.003 FAIM3 0.938 C7orf53 0.921 0.98 0.044 0.047 FAIM3 0.938 SLC15A2 0.824 0.992 0.046 0 FAIM3 0.938 IFI16 0.912 0.977 0.037 0.05 FAIM3 0.938 WSB2 0.911 0.985 0.045 0.02 FAIM3 0.938 HPSE 0.939 0.989 0.042 0.04 FAIM3 0.938 HS2ST1_UBA2 0.758 0.969 0.025 0.001 FAIM3 0.938 KLHL5 0.826 0.972 0.043 0.002 CLEC4E 0.883 PTGDR 0.919 0.963 0.022 0.032 CLEC4E 0.883 KLRF1 0.872 0.959 0.048 0 CLEC4E 0.883 CASS4 0.877 0.976 0.001 0.008 CLEC4E 0.883 ITK 0.899 0.98 0.005 0.003 CLEC4E 0.883 GOT2 0.905 0.96 0.018 0.038 CLEC4E 0.883 HIST1H4E 0.83 0.942 0.022 0.007 CLEC4E 0.883 CCR3 0.905 0.953 0.02 0.04 CLEC4E 0.883 CHI3L1 0.909 0.987 0.006 0.019 CLEC4E 0.883 GIMAP7 0.755 0.946 0.033 0 CLEC4E 0.883 RUNX2 0.848 0.969 0.003 0.005 CLEC4E 0.883 PHOSPHO1 0.85 0.947 0.009 0.025 CLEC4E 0.883 PDE3B 0.85 0.979 0.002 0.004 CLEC4E 0.883 SULF2 0.819 0.948 0.041 0.003 CLEC4E 0.883 IRS2 0.648 0.946 0.009 0 CLEC4E 0.883 NPCDR1 0.86 0.946 0.031 0.004 CLEC4E 0.883 CCR4 0.784 0.949 0.028 0 CLEC4E 0.883 ABCG1 0.789 0.937 0.026 0.008 CLEC4E 0.883 LASS4 0.794 0.919 0.036 0.031 CLEC4E 0.883 TSHZ2 0.801 0.965 0.005 0.001 CLEC4E 0.883 CAMK1D 0.702 0.947 0.023 0 IL18R1 0.933 LRRN3 0.947 0.997 0.01 0.036 IL18R1 0.933 VAMP2 0.673 0.962 0.036 0 IL18R1 0.933 CASS4 0.877 0.979 0.008 0.003 IL18R1 0.933 ITK 0.899 0.995 0.013 0.002 IL18R1 0.933 B3GAT3 0.783 0.984 0.017 0 IL18R1 0.933 HIST1H4E 0.83 0.967 0.042 0.002 IL18R1 0.933 GIMAP7 0.755 0.974 0.017 0 IL18R1 0.933 RUNX2 0.848 0.972 0.037 0.002 IL18R1 0.933 PMS2CL_PMS2 0.685 0.979 0.027 0 IL18R1 0.933 PDE3B 0.85 0.988 0.007 0.002 IL18R1 0.933 IRS2 0.648 0.958 0.034 0 IL18R1 0.933 KIAA1257_ACAD9/ 0.496 0.976 0.016 0 IL18R1 0.933 CCR4 0.784 0.976 0.013 0 IL18R1 0.933 RRP12_LOC644215 0.632 0.957 0.047 0 IL18R1 0.933 ABCG1 0.789 0.965 0.016 0.003 IL18R1 0.933 TSHZ2 0.801 0.975 0.026 0 ERLIN1 0.937 LRRN3 0.947 0.995 0.017 0.049 ERLIN1 0.937 CASS4 0.877 0.992 0.028 0.001 ERLIN1 0.937 ITK 0.899 0.991 0.028 0.003 ERLIN1 0.937 B3GAT3 0.783 0.979 0.036 0 ERLIN1 0.937 RUNX2 0.848 0.963 0.044 0.008 ERLIN1 0.937 PDE3B 0.85 0.987 0.016 0.002 ERLIN1 0.937 LASS4 0.794 0.957 0.05 0.005 ERLIN1 0.937 TSHZ2 0.801 0.98 0.012 0 FKBP5_LOC285847 0.841 KLRF1 0.872 0.935 0.022 0.005 FKBP5_LOC285847 0.841 SH2D1B 0.867 0.928 0.041 0.029 FKBP5_LOC285847 0.841 VAMP2 0.673 0.894 0.028 0.001 FKBP5_LOC285847 0.841 KLRK1_KLRC4 0.857 0.914 0.045 0.042 FKBP5_LOC285847 0.841 ITK 0.899 0.967 0.002 0.021 FKBP5_LOC285847 0.841 B3GAT3 0.783 0.908 0.024 0.012 FKBP5_LOC285847 0.841 GIMAP7 0.755 0.911 0.016 0.003 FKBP5_LOC285847 0.841 NA 0.696 0.902 0.039 0.001 FKBP5_LOC285847 0.841 PLA2G7 0.834 0.923 0.046 0.011 FKBP5_LOC285847 0.841 PMS2CL_PMS2 0.685 0.902 0.029 0 FKBP5_LOC285847 0.841 PDE3B 0.85 0.957 0.001 0.017 FKBP5_LOC285847 0.841 SULF2 0.819 0.913 0.043 0.036 FKBP5_LOC285847 0.841 IRS2 0.648 0.903 0.012 0.001 FKBP5_LOC285847 0.841 NPCDR1 0.86 0.941 0.003 0.012 FKBP5_LOC285847 0.841 LY6G5B_CSNK2B 0.661 0.915 0.013 0 FKBP5_LOC285847 0.841 CCR4 0.784 0.909 0.016 0.008 FKBP5_LOC285847 0.841 ABCG1 0.789 0.915 0.003 0.042 FKBP5_LOC285847 0.841 FAM118A 0.759 0.901 0.021 0.01 FKBP5_LOC285847 0.841 PYHIN1 0.807 0.922 0.033 0.006 FKBP5_LOC285847 0.841 NA 0.746 0.915 0.048 0 FKBP5_LOC285847 0.841 TSHZ2 0.801 0.944 0.002 0.004 FKBP5_LOC285847 0.841 RASA4_RASA4P_R 0.779 0.894 0.049 0.008 FKBP5_LOC285847 0.841 CAMK1D 0.702 0.921 0.007 0 FKBP5_LOC285847 0.841 CFD 0.767 0.911 0.048 0.003 C7orf53 0.921 PTGDR 0.919 0.979 0.04 0.006 C7orf53 0.921 CAMK4 0.953 0.997 0.006 0.045 C7orf53 0.921 LRRN3 0.947 0.992 0.005 0.045 C7orf53 0.921 CASS4 0.877 0.999 0.004 0 C7orf53 0.921 ITK 0.899 0.987 0.014 0.002 C7orf53 0.921 GOT2 0.905 0.976 0.039 0.008 C7orf53 0.921 HIST1H4E 0.83 0.967 0.04 0.001 C7orf53 0.921 CCR3 0.905 0.967 0.042 0.006 C7orf53 0.921 CHI3L1 0.909 0.978 0.023 0.026 C7orf53 0.921 RUNX2 0.848 0.994 0.007 0 C7orf53 0.921 PDE3B 0.85 0.996 0.005 0.001 C7orf53 0.921 NPCDR1 0.86 0.969 0.037 0.001 C7orf53 0.921 LY6G5B_CSNK2B 0.661 0.973 0.044 0 C7orf53 0.921 KIAA1257_ACAD9/ 0.496 0.98 0.022 0 C7orf53 0.921 CCR4 0.784 0.976 0.033 0 C7orf53 0.921 ABCG1 0.789 0.965 0.028 0.001 C7orf53 0.921 TSHZ2 0.801 0.974 0.035 0 C7orf53 0.921 CAMK1D 0.702 0.978 0.033 0 PLB1 0.938 RUNX2 0.848 0.989 0.034 0.002 PLB1 0.938 PDE3B 0.85 0.975 0.046 0.005 DSE 0.936 CAMK4 0.953 0.999 0.031 0.04 DSE 0.936 LRRN3 0.947 0.993 0.045 0.033 DSE 0.936 CASS4 0.877 0.974 0.013 0.008 DSE 0.936 RUNX2 0.848 0.974 0.019 0.004 DSE 0.936 PDE3B 0.85 0.974 0.019 0.006 DSE 0.936 LY6G5B_CSNK2B 0.661 0.972 0.039 0 PTGDR 0.919 FOXD4L3_FOXD4L6 0.892 0.991 0.019 0.006 PTGDR 0.919 SGMS2 0.859 0.971 0.038 0.005 PTGDR 0.919 PICALM 0.879 0.983 0.006 0.008 PTGDR 0.919 TLR10 0.905 0.972 0.021 0.022 PTGDR 0.919 JKAMP 0.93 0.986 0.004 0.047 PTGDR 0.919 WSB2 0.911 0.983 0.004 0.02 PTGDR 0.919 CLU 0.894 0.961 0.012 0.03 PTGDR 0.919 HPSE 0.939 0.987 0.014 0.026 PTGDR 0.919 PDGFC 0.837 0.969 0.018 0.013 PTGDR 0.919 HPGD 0.871 0.96 0.041 0.047 PTGDR 0.919 CDS2 0.869 0.954 0.022 0.049 PTGDR 0.919 HSPC159 0.823 0.97 0.016 0.001 PTGDR 0.919 PPP2R5A_SNORA16 0.775 0.958 0.032 0 PTGDR 0.919 EAF2_HCG11_LOC 0.917 0.974 0.039 0.046 PTGDR 0.919 RCBTB2_LOC10013 0.905 0.981 0.022 0.011 PTGDR 0.919 HMGB2 0.849 0.972 0.016 0.01 PTGDR 0.919 SIAE 0.814 0.956 0.031 0.002 PTGDR 0.919 ABCA13 0.868 0.96 0.034 0.009 PTGDR 0.919 AGTRAP 0.856 0.949 0.021 0.015 PTGDR 0.919 SUCNR1 0.922 0.979 0.007 0.034 PTGDR 0.919 TREML1 0.793 0.958 0.043 0.002 PTGDR 0.919 NEK6_LOC1001290 0.765 0.943 0.029 0.002 PTGDR 0.919 ANKRD28 0.8 0.968 0.009 0.001 CAMK4 0.953 DNAJC13 0.948 1 0.04 0.036 CAMK4 0.953 TNFAIP6 0.971 0.997 0.042 0.041 CAMK4 0.953 B3GNT5_MCF2L2 0.956 0.997 0.043 0.019 CAMK4 0.953 PICALM 0.879 0.999 0.043 0.001 CAMK4 0.953 C9orf72 0.958 0.999 0.044 0.039 CAMK4 0.953 SLC15A2 0.824 0.987 0.025 0 CAMK4 0.953 TLR10 0.905 1 0.04 0.003 CAMK4 0.953 IFI16 0.912 0.992 0.039 0.005 CAMK4 0.953 JKAMP 0.93 0.998 0.038 0.009 CAMK4 0.953 WSB2 0.911 0.986 0.021 0.01 CAMK4 0.953 HPSE 0.939 0.992 0.026 0.018 CAMK4 0.953 DDAH2 0.886 0.974 0.045 0.034 CAMK4 0.953 LRRC70_IPO11 0.923 0.994 0.048 0.011 CAMK4 0.953 TMEM144_LOC2855 0.92 0.999 0.042 0.016 CAMK4 0.953 EAF2_HCG11_LOC 0.917 0.998 0.043 0.018 CAMK4 0.953 RCBTB2_LOC10013 0.905 0.994 0.037 0.005 CAMK4 0.953 SLC39A9 0.551 0.975 0.037 0 CAMK4 0.953 LGALS1 0.736 0.977 0.022 0 CAMK4 0.953 SIAE 0.814 0.979 0.044 0 CAMK4 0.953 EFCAB2 0.96 0.999 0.044 0.023 CAMK4 0.953 AGTRAP 0.856 0.973 0.049 0.003 CAMK4 0.953 SUCNR1 0.922 0.993 0.04 0.009 CAMK4 0.953 MTRR 0.703 0.99 0.028 0 CAMK4 0.953 AIG1 0.842 0.993 0.042 0.001 CAMK4 0.953 HS2ST1_UBA2 0.758 0.995 0.047 0 CAMK4 0.953 NEK6_LOC1001290 0.765 0.972 0.027 0 CAMK4 0.953 GPR65 0.886 0.992 0.03 0.007 CAMK4 0.953 LRRFIP1 0.673 0.995 0.039 0 CAMK4 0.953 SFRS9 0.527 0.981 0.025 0 CAMK4 0.953 TAF13 0.854 0.995 0.044 0.001 CAMK4 0.953 KLHL5 0.826 0.997 0.035 0 CAMK4 0.953 ANKRD28 0.8 0.998 0.043 0 CAMK4 0.953 MGST3 0.602 0.986 0.038 0 CAMK4 0.953 CEP97 0.695 0.987 0.031 0 CAMK4 0.953 FAM118B 0.737 0.992 0.031 0 CAMK4 0.953 TMEM62_SPCS2_L 0.645 0.984 0.05 0 CAMK4 0.953 AIF1 0.816 0.993 0.05 0 DNAJC13 0.948 CASS4 0.877 0.982 0.028 0.003 DNAJC13 0.948 ITK 0.899 1 0.036 0.002 DNAJC13 0.948 GOT2 0.905 0.998 0.038 0.007 DNAJC13 0.948 HIST1H4E 0.83 0.981 0.041 0.001 DNAJC13 0.948 SLC39A9 0.551 0.978 0.02 0 DNAJC13 0.948 GIMAP7 0.755 0.997 0.042 0 DNAJC13 0.948 RUNX2 0.848 0.975 0.04 0.003 DNAJC13 0.948 LY6G5B_CSNK2B 0.661 0.979 0.03 0 DNAJC13 0.948 TSHZ2 0.801 0.99 0.043 0 TNFAIP6 0.971 LRRN3 0.947 0.998 0.035 0.03 TNFAIP6 0.971 HLA-DPB1 0.765 0.995 0.039 0 TNFAIP6 0.971 CASS4 0.877 0.998 0.031 0 TNFAIP6 0.971 GIMAP7 0.755 0.992 0.05 0 TNFAIP6 0.971 RUNX2 0.848 0.993 0.038 0.001 TNFAIP6 0.971 HLA-DPA1 0.65 0.995 0.036 0 TNFAIP6 0.971 PDE3B 0.85 0.997 0.035 0.001 FOXD4L3_FOXD4L6 0.892 SH2D1B 0.867 0.973 0.034 0.002 FOXD4L3_FOXD4L6 0.892 KLRK1_KLRC4 0.857 0.97 0.029 0.003 FOXD4L3_FOXD4L6 0.892 ITK 0.899 0.97 0.005 0.031 FOXD4L3_FOXD4L6 0.892 HIST1H4E 0.83 0.937 0.027 0.02 FOXD4L3_FOXD4L6 0.892 CCR3 0.905 0.97 0.005 0.034 FOXD4L3_FOXD4L6 0.892 CHI3L1 0.909 0.987 0.005 0.039 FOXD4L3_FOXD4L6 0.892 GIMAP7 0.755 0.951 0.011 0 FOXD4L3_FOXD4L6 0.892 ANAPC11 0.818 0.948 0.015 0.007 FOXD4L3_FOXD4L6 0.892 RUNX2 0.848 0.966 0.003 0.015 FOXD4L3_FOXD4L6 0.892 HLA-DRA 0.768 0.929 0.023 0.002 FOXD4L3_FOXD4L6 0.892 PDE3B 0.85 0.977 0.003 0.006 FOXD4L3_FOXD4L6 0.892 ITGA4_CERKL 0.694 0.946 0.041 0 FOXD4L3_FOXD4L6 0.892 NPCDR1 0.86 0.948 0.021 0.017 FOXD4L3_FOXD4L6 0.892 CCR4 0.784 0.939 0.037 0.001 FOXD4L3_FOXD4L6 0.892 ABCG1 0.789 0.946 0.021 0.007 FOXD4L3_FOXD4L6 0.892 PYHIN1 0.807 0.952 0.038 0.002 FOXD4L3_FOXD4L6 0.892 TSHZ2 0.801 0.952 0.013 0.002 FOXD4L3_FOXD4L6 0.892 RASA4_RASA4P_R 0.779 0.943 0.018 0.001 FOXD4L3_FOXD4L6 0.892 CAMK1D 0.702 0.937 0.045 0 MMP9_LOC1001280 0.887 CASS4 0.877 0.962 0.034 0.03 MMP9_LOC1001280 0.887 ITK 0.899 0.959 0.041 0.031 MMP9_LOC1001280 0.887 HIST1H4E 0.83 0.956 0.008 0.014 MMP9_LOC1001280 0.887 PHOSPHO1 0.85 0.957 0.049 0.025 MMP9_LOC1001280 0.887 RRP12_LOC644215 0.632 0.928 0.049 0 MMP9_LOC1001280 0.887 LASS4 0.794 0.935 0.022 0.027 GSR 0.831 KLRF1 0.872 0.927 0.031 0.005 GSR 0.831 SH2D1B 0.867 0.935 0.016 0.031 GSR 0.831 MRPL41 0.811 0.926 0.031 0.002 GSR 0.831 CASS4 0.877 0.981 0 0.002 GSR 0.831 KLRK1_KLRC4 0.857 0.943 0.01 0.009 GSR 0.831 ITK 0.899 0.985 0.001 0.002 GSR 0.831 GOT2 0.905 0.988 0 0.008 GSR 0.831 B3GAT3 0.783 0.925 0.012 0.002 GSR 0.831 CCR3 0.905 0.95 0.005 0.037 GSR 0.831 SLC39A9 0.551 0.916 0.025 0 GSR 0.831 GIMAP7 0.755 0.921 0.012 0.001 GSR 0.831 ANAPC11 0.818 0.908 0.028 0.026 GSR 0.831 RUNX2 0.848 0.95 0 0.022 GSR 0.831 HLA-DRA 0.768 0.893 0.046 0.013 GSR 0.831 PDE3B 0.85 0.949 0.009 0.003 GSR 0.831 SULF2 0.819 0.957 0.007 0.001 GSR 0.831 IRF4 0.781 0.933 0.01 0.002 GSR 0.831 HSP90AB1_HSP90A 0.799 0.933 0.023 0.002 GSR 0.831 NPCDR1 0.86 0.939 0.01 0.003 GSR 0.831 LY6G5B_CSNK2B 0.661 0.952 0.004 0 GSR 0.831 CCR4 0.784 0.91 0.044 0.003 GSR 0.831 FAM118A 0.759 0.94 0.01 0 GSR 0.831 PYHIN1 0.807 0.949 0.006 0.001 GSR 0.831 TSHZ2 0.801 0.962 0 0.001 GSR 0.831 RASA4_RASA4P_R 0.779 0.917 0.034 0.001 GSR 0.831 CAMK1D 0.702 0.941 0.005 0 KLRF1 0.872 ANKRD34B 0.846 0.95 0.004 0.013 KLRF1 0.872 SGMS2 0.859 0.957 0.001 0.032 KLRF1 0.872 GK3P_GK 0.877 0.952 0.006 0.012 KLRF1 0.872 SLC15A2 0.824 0.933 0.001 0.013 KLRF1 0.872 OLFM4 0.806 0.935 0.034 0.003 KLRF1 0.872 TCN1 0.816 0.953 0.002 0.004 KLRF1 0.872 ATP13A3 0.841 0.951 0.001 0.035 KLRF1 0.872 TMEM144_LOC2855 0.92 0.976 0.001 0.045 KLRF1 0.872 BPI 0.83 0.932 0.004 0.015 KLRF1 0.872 HSPC159 0.823 0.944 0.002 0.015 KLRF1 0.872 PPP2R5A_SNORA16 0.775 0.937 0.001 0.003 KLRF1 0.872 TMTC1 0.826 0.946 0.004 0.01 KLRF1 0.872 SEC24A_SAR1B 0.761 0.917 0.006 0.004 KLRF1 0.872 HMGB2 0.849 0.951 0.001 0.031 KLRF1 0.872 NA 0.866 0.958 0.001 0.02 KLRF1 0.872 LGALS1 0.736 0.901 0.018 0.011 KLRF1 0.872 CD163 0.735 0.927 0.009 0.002 KLRF1 0.872 MTHFS 0.757 0.916 0.015 0.003 KLRF1 0.872 LCN2 0.752 0.921 0.026 0.001 KLRF1 0.872 EIF2AK2 0.857 0.948 0.008 0.013 KLRF1 0.872 SIAE 0.814 0.92 0.006 0.026 KLRF1 0.872 CDA 0.804 0.912 0.018 0.029 KLRF1 0.872 SAP30 0.728 0.903 0.005 0.011 KLRF1 0.872 MTRR 0.703 0.912 0.048 0 KLRF1 0.872 AIG1 0.842 0.928 0.001 0.048 KLRF1 0.872 HS2ST1_UBA2 0.758 0.904 0.043 0.01 KLRF1 0.872 TREML1 0.793 0.932 0.006 0.01 KLRF1 0.872 GSTO1 0.793 0.928 0.001 0.015 KLRF1 0.872 MACF1 0.742 0.919 0.037 0 KLRF1 0.872 AMFR 0.69 0.903 0.017 0.003 KLRF1 0.872 OR9A2 0.813 0.926 0.005 0.01 KLRF1 0.872 HDHD1A 0.63 0.915 0.006 0 KLRF1 0.872 ACTA2 0.783 0.922 0.01 0.013 KLRF1 0.872 DPH3 0.86 0.943 0.003 0.022 KLRF1 0.872 ZNF28 0.558 0.911 0.033 0 KLRF1 0.872 TAF13 0.854 0.95 0.004 0.02 KLRF1 0.872 P4HA1_RPL17 0.715 0.931 0.01 0 KLRF1 0.872 KLHL5 0.826 0.932 0.005 0.019 KLRF1 0.872 DLEU2_DLEU2L 0.874 0.958 0.001 0.022 KLRF1 0.872 ANKRD28 0.8 0.942 0.001 0.006 KLRF1 0.872 HSPB1_HSPBL2 0.778 0.911 0.01 0.014 KLRF1 0.872 CCDC125 0.632 0.912 0.041 0 KLRF1 0.872 LHFP 0.759 0.903 0.015 0.005 KLRF1 0.872 ZRANB1 0.724 0.941 0.001 0 KLRF1 0.872 FSD1L_GARNL1 0.66 0.906 0.034 0 KLRF1 0.872 UBE2F_C20orf194 0.762 0.924 0.007 0.002 KLRF1 0.872 MYL9 0.771 0.921 0.013 0.009 KLRF1 0.872 CYP4F3_CYP4F2 0.593 0.925 0.005 0 KLRF1 0.872 CEP97 0.695 0.923 0.01 0 KLRF1 0.872 DYNLL1 0.776 0.947 0.003 0 KLRF1 0.872 PLIN2 0.671 0.915 0.003 0 KLRF1 0.872 FAM118B 0.737 0.941 0.001 0.001 KLRF1 0.872 NP 0.633 0.901 0.038 0 KLRF1 0.872 SPARC 0.703 0.932 0.008 0 KLRF1 0.872 PLEKHA3 0.658 0.921 0.006 0 KLRF1 0.872 TMEM62_SPCS2_L 0.645 0.929 0.003 0 KLRF1 0.872 PLEKHF2 0.822 0.941 0.004 0.011 KLRF1 0.872 CMTM5 0.694 0.906 0.035 0.001 KLRF1 0.872 AIF1 0.816 0.933 0.001 0.022 KLRF1 0.872 HIST2H2BF_HIST2 0.754 0.916 0.031 0.002 KLRF1 0.872 CALM2_C2orf61 0.733 0.934 0.003 0 KLRF1 0.872 SPATA6 0.642 0.917 0.01 0 KLRF1 0.872 DCTN5 0.557 0.903 0.05 0 SH2D1B 0.867 ANKRD34B 0.846 0.938 0.018 0.033 SH2D1B 0.867 SGMS2 0.859 0.974 0.003 0.008 SH2D1B 0.867 GK3P_GK 0.877 0.947 0.025 0.018 SH2D1B 0.867 SLC15A2 0.824 0.966 0.002 0.002 SH2D1B 0.867 TCN1 0.816 0.959 0.028 0.001 SH2D1B 0.867 PLAC8 0.767 0.922 0.027 0.008 SH2D1B 0.867 ATP13A3 0.841 0.96 0.005 0.02 SH2D1B 0.867 HSPC159 0.823 0.938 0.008 0.023 SH2D1B 0.867 PPP2R5A_SNORA16 0.775 0.939 0.008 0.003 SH2D1B 0.867 SEC24A_SAR1B 0.761 0.921 0.018 0.006 SH2D1B 0.867 HMGB2 0.849 0.962 0.016 0.012 SH2D1B 0.867 SLC39A9 0.551 0.908 0.042 0 SH2D1B 0.867 NA 0.866 0.948 0.006 0.03 SH2D1B 0.867 LGALS1 0.736 0.914 0.021 0.007 SH2D1B 0.867 EIF2AK2 0.857 0.948 0.005 0.028 SH2D1B 0.867 ABCA13 0.868 0.959 0.017 0.01 SH2D1B 0.867 CDA 0.804 0.928 0.023 0.015 SH2D1B 0.867 SAP30 0.728 0.903 0.009 0.013 SH2D1B 0.867 SUCNR1 0.922 0.985 0.003 0.022 SH2D1B 0.867 MTRR 0.703 0.928 0.002 0.001 SH2D1B 0.867 AIG1 0.842 0.961 0.006 0.008 SH2D1B 0.867 HS2ST1_UBA2 0.758 0.91 0.03 0.015 SH2D1B 0.867 GSTO1 0.793 0.944 0.006 0.008 SH2D1B 0.867 MACF1 0.742 0.93 0.022 0.001 SH2D1B 0.867 AMFR 0.69 0.897 0.046 0.006 SH2D1B 0.867 HDHD1A 0.63 0.93 0.016 0 SH2D1B 0.867 DPH3 0.86 0.948 0.011 0.026 SH2D1B 0.867 TAF13 0.854 0.972 0.001 0.01 SH2D1B 0.867 P4HA1_RPL17 0.715 0.941 0.011 0 SH2D1B 0.867 KLHL5 0.826 0.946 0.003 0.022 SH2D1B 0.867 DLEU2_DLEU2L 0.874 0.954 0.003 0.041 SH2D1B 0.867 ANKRD28 0.8 0.948 0.004 0.005 SH2D1B 0.867 MGST3 0.602 0.932 0.019 0 SH2D1B 0.867 HSPB1_HSPBL2 0.778 0.917 0.008 0.014 SH2D1B 0.867 ZRANB1 0.724 0.913 0.019 0.002 SH2D1B 0.867 UBE2F_C20orf194 0.762 0.943 0.01 0.001 SH2D1B 0.867 CCRL2 0.786 0.935 0.033 0.004 SH2D1B 0.867 MYL9 0.771 0.917 0.021 0.013 SH2D1B 0.867 CEP97 0.695 0.943 0.025 0 SH2D1B 0.867 DYNLL1 0.776 0.956 0.013 0 SH2D1B 0.867 PLIN2 0.671 0.934 0.012 0 SH2D1B 0.867 FAM118B 0.737 0.951 0.003 0.001 SH2D1B 0.867 CTSL1_CTSLL3 0.715 0.939 0.041 0 SH2D1B 0.867 PLEKHA3 0.658 0.919 0.027 0 SH2D1B 0.867 TMEM62_SPCS2_L 0.645 0.929 0.006 0 SH2D1B 0.867 PLEKHF2 0.822 0.957 0.004 0.006 SH2D1B 0.867 AIF1 0.816 0.941 0.008 0.021 SH2D1B 0.867 HIST2H2BF_HIST2 0.754 0.918 0.022 0.004 SH2D1B 0.867 CALM2_C2orf61 0.733 0.957 0.005 0 SH2D1B 0.867 DCTN5 0.557 0.916 0.019 0 ANKRD34B 0.846 HIST1H4C 0.859 0.944 0.006 0.026 ANKRD34B 0.846 HIST1H3I 0.787 0.921 0.022 0.003 ANKRD34B 0.846 MRPL41 0.811 0.952 0.01 0 ANKRD34B 0.846 KLRK1_KLRC4 0.857 0.952 0.005 0.017 ANKRD34B 0.846 B3GAT3 0.783 0.911 0.006 0.015 ANKRD34B 0.846 HIST1H4E 0.83 0.934 0.002 0.028 ANKRD34B 0.846 FGFBP2 0.812 0.938 0.019 0.003 ANKRD34B 0.846 KLRD1 0.784 0.932 0.021 0.002 ANKRD34B 0.846 GIMAP7 0.755 0.908 0.027 0.006 ANKRD34B 0.846 ANAPC11 0.818 0.924 0.008 0.028 ANKRD34B 0.846 DNAJC9_FAM149B1 0.811 0.952 0.013 0.001 ANKRD34B 0.846 PLA2G7 0.834 0.938 0.024 0.006 ANKRD34B 0.846 SULF2 0.819 0.923 0.015 0.014 ANKRD34B 0.846 IRF4 0.781 0.914 0.006 0.023 ANKRD34B 0.846 HSP90AB1_HSP90A 0.799 0.921 0.043 0.015 ANKRD34B 0.846 NPCDR1 0.86 0.921 0.008 0.034 ANKRD34B 0.846 CCR4 0.784 0.917 0.02 0.005 ANKRD34B 0.846 PYHIN1 0.807 0.931 0.006 0.015 ANKRD34B 0.846 TSHZ2 0.801 0.938 0.002 0.006 SGMS2 0.859 CRIP1 0.83 0.95 0.041 0.001 SGMS2 0.859 MRPL41 0.811 0.957 0.018 0 SGMS2 0.859 CASS4 0.877 0.962 0.001 0.027 SGMS2 0.859 KLRK1_KLRC4 0.857 0.951 0.022 0.009 SGMS2 0.859 ITK 0.899 0.984 0.001 0.01 SGMS2 0.859 GOT2 0.905 0.976 0.002 0.036 SGMS2 0.859 B3GAT3 0.783 0.936 0.018 0.003 SGMS2 0.859 HIST1H4E 0.83 0.938 0.004 0.021 SGMS2 0.859 CHI3L1 0.909 0.99 0.002 0.028 SGMS2 0.859 SLC39A9 0.551 0.91 0.043 0 SGMS2 0.859 GIMAP7 0.755 0.959 0.004 0 SGMS2 0.859 ANAPC11 0.818 0.943 0.033 0.002 SGMS2 0.859 RUNX2 0.848 0.965 0.003 0.007 SGMS2 0.859 DNAJC9_FAM149B1 0.811 0.959 0.046 0 SGMS2 0.859 PLA2G7 0.834 0.955 0.026 0.001 SGMS2 0.859 PDE3B 0.85 0.976 0.003 0.003 SGMS2 0.859 SULF2 0.819 0.978 0.003 0.001 SGMS2 0.859 IRF4 0.781 0.923 0.047 0.01 SGMS2 0.859 HSP90AB1_HSP90A 0.799 0.929 0.045 0.014 SGMS2 0.859 NPCDR1 0.86 0.942 0.014 0.013 SGMS2 0.859 LY6G5B_CSNK2B 0.661 0.933 0.005 0 SGMS2 0.859 CCR4 0.784 0.969 0.003 0 SGMS2 0.859 FAM118A 0.759 0.931 0.002 0.005 SGMS2 0.859 PYHIN1 0.807 0.948 0.017 0.003 SGMS2 0.859 CLC 0.81 0.943 0.048 0.003 SGMS2 0.859 TSHZ2 0.801 0.971 0.002 0.001 SGMS2 0.859 RASA4_RASA4P_R 0.779 0.928 0.031 0.002 SGMS2 0.859 CAMK1D 0.702 0.938 0.013 0 SGMS2 0.859 IL5RA 0.832 0.939 0.05 0.004 B3GNT5_MCF2L2 0.956 LRRN3 0.947 0.999 0.018 0.028 B3GNT5_MCF2L2 0.956 VAMP2 0.673 0.985 0.016 0 B3GNT5_MCF2L2 0.956 CASS4 0.877 0.998 0.014 0 B3GNT5_MCF2L2 0.956 RUNX2 0.848 0.986 0.023 0.001 B3GNT5_MCF2L2 0.956 PDE3B 0.85 0.998 0.015 0.001 GK3P_GK 0.877 CRIP1 0.83 0.932 0.037 0.015 GK3P_GK 0.877 VAMP2 0.673 0.911 0.015 0.001 GK3P_GK 0.877 MRPL41 0.811 0.951 0.006 0.001 GK3P_GK 0.877 CASS4 0.877 0.953 0.002 0.047 GK3P_GK 0.877 B3GAT3 0.783 0.935 0.003 0.005 GK3P_GK 0.877 HIST1H4E 0.83 0.928 0.006 0.035 GK3P_GK 0.877 CCR3 0.905 0.959 0.002 0.043 GK3P_GK 0.877 CHI3L1 0.909 0.96 0.012 0.048 GK3P_GK 0.877 GIMAP7 0.755 0.924 0.009 0.001 GK3P_GK 0.877 ANAPC11 0.818 0.93 0.013 0.008 GK3P_GK 0.877 HLA-DRA 0.768 0.907 0.047 0.007 GK3P_GK 0.877 DNAJC9_FAM149B1 0.811 0.943 0.034 0.003 GK3P_GK 0.877 NA 0.696 0.931 0.013 0 GK3P_GK 0.877 PLA2G7 0.834 0.944 0.02 0.004 GK3P_GK 0.877 PMS2CL_PMS2 0.685 0.915 0.02 0 GK3P_GK 0.877 PDE3B 0.85 0.949 0.001 0.041 GK3P_GK 0.877 SULF2 0.819 0.922 0.038 0.031 GK3P_GK 0.877 HSP90AB1_HSP90A 0.799 0.925 0.038 0.011 GK3P_GK 0.877 NPCDR1 0.86 0.942 0.009 0.017 GK3P_GK 0.877 LY6G5B_CSNK2B 0.661 0.931 0.013 0 GK3P_GK 0.877 CCR4 0.784 0.938 0.002 0.003 GK3P_GK 0.877 ABCG1 0.789 0.91 0.033 0.041 GK3P_GK 0.877 FAM118A 0.759 0.913 0.041 0.004 GK3P_GK 0.877 CLC 0.81 0.933 0.019 0.014 GK3P_GK 0.877 TSHZ2 0.801 0.94 0.004 0.005 GK3P_GK 0.877 RASA4_RASA4P_R 0.779 0.921 0.021 0.003 GK3P_GK 0.877 CAMK1D 0.702 0.927 0.009 0 GK3P_GK 0.877 CFD 0.767 0.951 0.003 0.001 GK3P_GK 0.877 IL5RA 0.832 0.935 0.017 0.016 PFKFB2 0.874 MRPL41 0.811 0.954 0.025 0.002 PFKFB2 0.874 ITK 0.899 0.962 0.015 0.047 PFKFB2 0.874 B3GAT3 0.783 0.954 0.019 0.001 PFKFB2 0.874 HIST1H4E 0.83 0.937 0.023 0.025 PFKFB2 0.874 DNAJC9_FAM149B1 0.811 0.961 0.04 0.001 PFKFB2 0.874 PMS2CL_PMS2 0.685 0.935 0.035 0 PFKFB2 0.874 PDE3B 0.85 0.948 0.013 0.045 PFKFB2 0.874 IRF4 0.781 0.929 0.024 0.014 PFKFB2 0.874 IRS2 0.648 0.925 0.039 0 PFKFB2 0.874 HSP90AB1_HSP90A 0.799 0.954 0.044 0.003 PFKFB2 0.874 ABCG1 0.789 0.927 0.017 0.033 PFKFB2 0.874 FAM118A 0.759 0.935 0.032 0.001 PFKFB2 0.874 TSHZ2 0.801 0.946 0.019 0.005 PFKFB2 0.874 CAMK1D 0.702 0.93 0.05 0 PICALM 0.879 LRRN3 0.947 0.993 0.002 0.033 PICALM 0.879 VAMP2 0.673 0.952 0.015 0 PICALM 0.879 CASS4 0.877 0.989 0.002 0.001 PICALM 0.879 ITK 0.899 0.983 0.004 0.002 PICALM 0.879 GOT2 0.905 0.976 0.013 0.005 PICALM 0.879 B3GAT3 0.783 0.953 0.047 0 PICALM 0.879 HIST1H4E 0.83 0.962 0.019 0 PICALM 0.879 CCR3 0.905 0.967 0.01 0.009 PICALM 0.879 CHI3L1 0.909 0.975 0.006 0.039 PICALM 0.879 GIMAP7 0.755 0.958 0.018 0 PICALM 0.879 ANAPC11 0.818 0.96 0.033 0 PICALM 0.879 RUNX2 0.848 0.989 0.002 0.001 PICALM 0.879 PDE3B 0.85 0.989 0.002 0.001 PICALM 0.879 LY6G5B_CSNK2B 0.661 0.948 0.039 0 PICALM 0.879 CCR4 0.784 0.972 0.014 0 PICALM 0.879 TSHZ2 0.801 0.947 0.048 0 METTL7B 0.943 GOT2 0.905 0.983 0.047 0.014 METTL7B 0.943 HIST1H4E 0.83 0.978 0.043 0.001 HIST1H4C 0.859 JKAMP 0.93 0.982 0.001 0.036 HIST1H4C 0.859 CD63 0.757 0.953 0.005 0.001 HIST1H4C 0.859 ATP13A3 0.841 0.928 0.044 0.045 HIST1H4C 0.859 HSPC159 0.823 0.937 0.036 0.006 HIST1H4C 0.859 PPP2R5A_SNORA16 0.775 0.925 0.038 0.002 HIST1H4C 0.859 TMTC1 0.826 0.923 0.032 0.018 HIST1H4C 0.859 HMGB2 0.849 0.95 0.029 0.007 HIST1H4C 0.859 EXOSC4 0.731 0.935 0.009 0.001 HIST1H4C 0.859 FOLR3_FOLR2 0.848 0.959 0.012 0.001 HIST1H4C 0.859 LGALS1 0.736 0.929 0.034 0.001 HIST1H4C 0.859 SIAE 0.814 0.942 0.003 0.008 HIST1H4C 0.859 ABCA13 0.868 0.985 0.003 0 HIST1H4C 0.859 CDA 0.804 0.927 0.037 0.007 HIST1H4C 0.859 SAP30 0.728 0.895 0.029 0.008 HIST1H4C 0.859 AGTRAP 0.856 0.955 0.001 0.039 HIST1H4C 0.859 SUCNR1 0.922 0.996 0.001 0.006 HIST1H4C 0.859 AIG1 0.842 0.945 0.025 0.004 HIST1H4C 0.859 PCOLCE2 0.709 0.937 0.009 0 HIST1H4C 0.859 VOPP1_LOC100128 0.618 0.907 0.037 0 HIST1H4C 0.859 SLC39A8 0.698 0.933 0.02 0 HIST1H4C 0.859 SLC11A1 0.785 0.917 0.025 0.017 HIST1H4C 0.859 TREML1 0.793 0.952 0.012 0.002 HIST1H4C 0.859 GSTO1 0.793 0.926 0.046 0.005 HIST1H4C 0.859 CLEC4A 0.87 0.933 0.027 0.043 HIST1H4C 0.859 NEK6_LOC1001290 0.765 0.906 0.024 0.011 HIST1H4C 0.859 AMFR 0.69 0.908 0.046 0.002 HIST1H4C 0.859 DPH3 0.86 0.966 0.001 0.006 HIST1H4C 0.859 TAF13 0.854 0.952 0.048 0.003 HIST1H4C 0.859 DLEU2_DLEU2L 0.874 0.955 0.003 0.016 HIST1H4C 0.859 ANKRD28 0.8 0.932 0.036 0.005 HIST1H4C 0.859 HSPB1_HSPBL2 0.778 0.915 0.046 0.006 HIST1H4C 0.859 LHFP 0.759 0.907 0.027 0.005 HIST1H4C 0.859 UBE2F_C20orf194 0.762 0.952 0.014 0 HIST1H4C 0.859 MYL9 0.771 0.95 0.003 0.002 HIST1H4C 0.859 CMTM5 0.694 0.947 0.007 0 HIST1H4C 0.859 CALM2_C2orf61 0.733 0.94 0.031 0 C9orf72 0.958 LRRN3 0.947 0.998 0.05 0.026 C9orf72 0.958 CASS4 0.877 0.998 0.043 0 C9orf72 0.958 GIMAP7 0.755 0.989 0.041 0 C9orf72 0.958 RUNX2 0.848 0.995 0.044 0.001 C9orf72 0.958 PDE3B 0.85 0.999 0.04 0.001 HIST1H3I 0.787 TCN1 0.816 0.899 0.044 0.006 HIST1H3I 0.787 CD63 0.757 0.901 0.002 0.015 HIST1H3I 0.787 BPI 0.83 0.913 0.025 0.002 HIST1H3I 0.787 PPP2R5A_SNORA16 0.775 0.878 0.015 0.043 HIST1H3I 0.787 SEC24A_SAR1B 0.761 0.866 0.033 0.039 HIST1H3I 0.787 HMGB2 0.849 0.917 0.01 0.047 HIST1H3I 0.787 EXOSC4 0.731 0.888 0.007 0.017 HIST1H3I 0.787 FOLR3_FOLR2 0.848 0.924 0.003 0.018 HIST1H3I 0.787 LGALS1 0.736 0.87 0.017 0.033 HIST1H3I 0.787 ABCA13 0.868 0.952 0.001 0.007 HIST1H3I 0.787 SUCNR1 0.922 0.981 0 0.025 HIST1H3I 0.787 PCOLCE2 0.709 0.886 0.003 0.005 HIST1H3I 0.787 VOPP1_LOC100128 0.618 0.844 0.02 0.003 HIST1H3I 0.787 SLC39A8 0.698 0.875 0.005 0.009 HIST1H3I 0.787 TREML1 0.793 0.892 0.035 0.025 HIST1H3I 0.787 RETN 0.663 0.858 0.043 0.002 HIST1H3I 0.787 DPH3 0.86 0.936 0 0.044 HIST1H3I 0.787 TAF13 0.854 0.932 0.003 0.026 HIST1H3I 0.787 HSPB1_HSPBL2 0.778 0.884 0.005 0.044 HIST1H3I 0.787 LHFP 0.759 0.866 0.005 0.047 HIST1H3I 0.787 UBE2F_C20orf194 0.762 0.903 0.014 0.003 HIST1H3I 0.787 MYL9 0.771 0.912 0.003 0.011 HIST1H3I 0.787 CTSL1_CTSLL3 0.715 0.874 0.045 0.003 HIST1H3I 0.787 PLEKHA3 0.658 0.857 0.039 0.002 HIST1H3I 0.787 CMTM5 0.694 0.877 0.045 0.002 SLC15A2 0.824 LRRN3 0.947 0.979 0 0.023 SLC15A2 0.824 HLA-DPB1 0.765 0.917 0.011 0 SLC15A2 0.824 VAMP2 0.673 0.905 0.01 0 SLC15A2 0.824 MRPL41 0.811 0.926 0.015 0.002 SLC15A2 0.824 CASS4 0.877 0.959 0 0.017 SLC15A2 0.824 KLRK1_KLRC4 0.857 0.95 0.004 0.004 SLC15A2 0.824 ITK 0.899 0.998 0 0.002 SLC15A2 0.824 GOT2 0.905 0.979 0 0.018 SLC15A2 0.824 B3GAT3 0.783 0.931 0.004 0.002 SLC15A2 0.824 FGFBP2 0.812 0.907 0.044 0.006 SLC15A2 0.824 CCR3 0.905 0.957 0.001 0.021 SLC15A2 0.824 SLC39A9 0.551 0.902 0.022 0 SLC15A2 0.824 GIMAP7 0.755 0.946 0.001 0 SLC15A2 0.824 ANAPC11 0.818 0.914 0.007 0.018 SLC15A2 0.824 RUNX2 0.848 0.946 0.001 0.01 SLC15A2 0.824 HLA-DRA 0.768 0.926 0.006 0.001 SLC15A2 0.824 DNAJC9_FAM149B1 0.811 0.947 0.014 0 SLC15A2 0.824 NA 0.696 0.924 0.008 0 SLC15A2 0.824 PLA2G7 0.834 0.934 0.018 0.001 SLC15A2 0.824 PDE3B 0.85 0.941 0.006 0.007 SLC15A2 0.824 SULF2 0.819 0.92 0.012 0.008 SLC15A2 0.824 IRF4 0.781 0.966 0.001 0 SLC15A2 0.824 HSP90AB1_HSP90A 0.799 0.942 0.006 0.002 SLC15A2 0.824 ITGA4_CERKL 0.694 0.933 0.012 0 SLC15A2 0.824 NPCDR1 0.86 0.956 0.002 0.001 SLC15A2 0.824 LY6G5B_CSNK2B 0.661 0.948 0.001 0 SLC15A2 0.824 CCR4 0.784 0.949 0.002 0 SLC15A2 0.824 ABCG1 0.789 0.913 0.008 0.015 SLC15A2 0.824 FAM118A 0.759 0.923 0.008 0 SLC15A2 0.824 PYHIN1 0.807 0.965 0.002 0 SLC15A2 0.824 CLC 0.81 0.929 0.014 0.005 SLC15A2 0.824 SYNE2 0.678 0.945 0.006 0 SLC15A2 0.824 NA 0.746 0.92 0.022 0 SLC15A2 0.824 TSHZ2 0.801 0.953 0.001 0.001 SLC15A2 0.824 RASA4_RASA4P_R 0.779 0.93 0.003 0.001 SLC15A2 0.824 CAMK1D 0.702 0.919 0.013 0 SLC15A2 0.824 LOC100128751 0.651 0.903 0.038 0 SLC15A2 0.824 IL5RA 0.832 0.922 0.031 0.002 TLR10 0.905 VAMP2 0.673 0.952 0.011 0 TLR10 0.905 CASS4 0.877 0.983 0.004 0.002 TLR10 0.905 ITK 0.899 0.983 0.006 0.004 TLR10 0.905 GOT2 0.905 0.971 0.022 0.017 TLR10 0.905 HIST1H4E 0.83 0.958 0.018 0.003 TLR10 0.905 CCR3 0.905 0.965 0.025 0.01 TLR10 0.905 CHI3L1 0.909 0.968 0.033 0.035 TLR10 0.905 GIMAP7 0.755 0.964 0.018 0 TLR10 0.905 ANAPC11 0.818 0.966 0.034 0 TLR10 0.905 RUNX2 0.848 0.972 0.005 0.005 TLR10 0.905 PDE3B 0.85 0.984 0.002 0.002 TLR10 0.905 NPCDR1 0.86 0.96 0.034 0.002 TLR10 0.905 LY6G5B_CSNK2B 0.661 0.969 0.011 0 TLR10 0.905 CCR4 0.784 0.979 0.003 0 TLR10 0.905 TSHZ2 0.801 0.962 0.017 0 TLR10 0.905 RASA4_RASA4P_R 0.779 0.949 0.042 0 TLR10 0.905 CAMK1D 0.702 0.969 0.015 0 ADM 0.963 PPIF 0.614 0.989 0.035 0 CD274 0.966 CCR4 0.784 0.984 0.049 0 CRIP1 0.83 CD63 0.757 0.917 0.003 0.012 CRIP1 0.83 PPP2R5A_SNORA16 0.775 0.899 0.015 0.023 CRIP1 0.83 SEC24A_SAR1B 0.761 0.892 0.007 0.027 CRIP1 0.83 SH3PXD2B 0.734 0.885 0.019 0.005 CRIP1 0.83 HMGB2 0.849 0.957 0.002 0.023 CRIP1 0.83 EXOSC4 0.731 0.888 0.018 0.017 CRIP1 0.83 LGALS1 0.736 0.908 0.003 0.016 CRIP1 0.83 MTHFS 0.757 0.893 0.013 0.029 CRIP1 0.83 ABCA13 0.868 0.952 0.008 0.006 CRIP1 0.83 SAP30 0.728 0.868 0.01 0.035 CRIP1 0.83 SUCNR1 0.922 0.986 0 0.02 CRIP1 0.83 AIG1 0.842 0.94 0.005 0.016 CRIP1 0.83 PCOLCE2 0.709 0.871 0.035 0.011 CRIP1 0.83 SLC11A1 0.785 0.904 0.006 0.048 CRIP1 0.83 TREML1 0.793 0.901 0.049 0.043 CRIP1 0.83 GSTO1 0.793 0.919 0.011 0.017 CRIP1 0.83 NEK6_LOC1001290 0.765 0.899 0.009 0.027 CRIP1 0.83 AMFR 0.69 0.878 0.02 0.017 CRIP1 0.83 OR9A2 0.813 0.915 0.01 0.026 CRIP1 0.83 TAF13 0.854 0.921 0.032 0.045 CRIP1 0.83 ANKRD28 0.8 0.911 0.003 0.04 CRIP1 0.83 MGST3 0.602 0.894 0.046 0 CRIP1 0.83 LHFP 0.759 0.86 0.038 0.045 CRIP1 0.83 UBE2F_C20orf194 0.762 0.916 0.013 0.004 CRIP1 0.83 MYL9 0.771 0.905 0.007 0.024 CRIP1 0.83 CEP97 0.695 0.895 0.033 0.001 CRIP1 0.83 DYNLL1 0.776 0.949 0.006 0 CRIP1 0.83 FAM118B 0.737 0.903 0.02 0.005 CRIP1 0.83 CDC26 0.619 0.88 0.049 0 CRIP1 0.83 CALM2_C2orf61 0.733 0.931 0.002 0.001 LRRN3 0.947 IFI16 0.912 0.989 0.016 0.01 LRRN3 0.947 JKAMP 0.93 0.996 0.025 0.013 LRRN3 0.947 SLC1A3 0.896 0.975 0.047 0.014 LRRN3 0.947 WSB2 0.911 0.975 0.026 0.029 LRRN3 0.947 HPSE 0.939 0.988 0.013 0.035 LRRN3 0.947 DDAH2 0.886 0.976 0.02 0.041 LRRN3 0.947 LRRC70_IPO11 0.923 0.994 0.042 0.013 LRRN3 0.947 TMEM144_LOC2855 0.92 0.997 0.035 0.018 LRRN3 0.947 PPP2R5A_SNORA16 0.775 0.988 0.047 0 LRRN3 0.947 EAF2_HCG11_LOC 0.917 0.996 0.02 0.024 LRRN3 0.947 RCBTB2_LOC10013 0.905 0.988 0.018 0.015 LRRN3 0.947 SEC24A_SAR1B 0.761 0.979 0.021 0 LRRN3 0.947 EXOSC4 0.731 0.974 0.046 0 LRRN3 0.947 NA 0.866 0.994 0.039 0.001 LRRN3 0.947 LGALS1 0.736 0.969 0.041 0 LRRN3 0.947 CCR1 0.897 0.976 0.042 0.011 LRRN3 0.947 FFAR2 0.868 0.976 0.036 0.003 LRRN3 0.947 EIF2AK2 0.857 0.99 0.025 0.001 LRRN3 0.947 SIAE 0.814 0.976 0.03 0.001 LRRN3 0.947 EFCAB2 0.96 0.999 0.03 0.022 LRRN3 0.947 AGTRAP 0.856 0.974 0.019 0.005 LRRN3 0.947 SUCNR1 0.922 0.993 0.017 0.012 LRRN3 0.947 MTRR 0.703 0.978 0.04 0 LRRN3 0.947 AIG1 0.842 0.986 0.024 0.001 LRRN3 0.947 GSTO1 0.793 0.978 0.023 0.001 LRRN3 0.947 CLEC4A 0.87 0.988 0.023 0.004 LRRN3 0.947 GPR65 0.886 0.984 0.021 0.013 LRRN3 0.947 MACF1 0.742 0.983 0.048 0 LRRN3 0.947 HDHD1A 0.63 0.985 0.043 0 LRRN3 0.947 TRIM21 0.874 0.977 0.027 0.009 LRRN3 0.947 TAF13 0.854 0.989 0.034 0.002 LRRN3 0.947 P4HA1_RPL17 0.715 0.984 0.03 0 LRRN3 0.947 KLHL5 0.826 0.985 0.029 0.001 LRRN3 0.947 MGST3 0.602 0.977 0.03 0 LRRN3 0.947 FAM118B 0.737 0.982 0.036 0 LRRN3 0.947 TMEM62_SPCS2_L 0.645 0.978 0.033 0 LRRN3 0.947 AIF1 0.816 0.987 0.022 0.001 HLA-DPB1 0.765 IFI16 0.912 0.975 0 0.025 HLA-DPB1 0.765 PLAC8 0.767 0.864 0.05 0.043 HLA-DPB1 0.765 PPP2R5A_SNORA16 0.775 0.868 0.019 0.029 HLA-DPB1 0.765 SEC24A_SAR1B 0.761 0.895 0 0.017 HLA-DPB1 0.765 NA 0.866 0.931 0.001 0.036 HLA-DPB1 0.765 FFAR2 0.868 0.936 0 0.026 HLA-DPB1 0.765 EIF2AK2 0.857 0.947 0.001 0.007 HLA-DPB1 0.765 AIG1 0.842 0.912 0.002 0.047 HLA-DPB1 0.765 GAB2 0.729 0.849 0.016 0.035 HLA-DPB1 0.765 GSTO1 0.793 0.899 0.001 0.041 HLA-DPB1 0.765 CLEC4A 0.87 0.946 0 0.034 HLA-DPB1 0.765 TRAF3 0.594 0.856 0.037 0 HLA-DPB1 0.765 MACF1 0.742 0.859 0.029 0.007 HLA-DPB1 0.765 TRIM21 0.874 0.978 0 0.011 HLA-DPB1 0.765 ICAM1 0.821 0.943 0 0.01 HLA-DPB1 0.765 TAF13 0.854 0.907 0.004 0.032 HLA-DPB1 0.765 P4HA1_RPL17 0.715 0.884 0.024 0 HLA-DPB1 0.765 KLHL5 0.826 0.922 0 0.018 HLA-DPB1 0.765 HSPB1_HSPBL2 0.778 0.888 0.005 0.023 HLA-DPB1 0.765 LAIR1_LAIR2 0.749 0.855 0.025 0.04 HLA-DPB1 0.765 UBE2F_C20orf194 0.762 0.862 0.021 0.02 HLA-DPB1 0.765 CCRL2 0.786 0.916 0.006 0.004 HLA-DPB1 0.765 IRF1 0.651 0.876 0.008 0.001 HLA-DPB1 0.765 DYNLL1 0.776 0.89 0.008 0.003 HLA-DPB1 0.765 FAM118B 0.737 0.872 0.048 0.003 HLA-DPB1 0.765 CTSL1_CTSLL3 0.715 0.886 0.006 0.002 HLA-DPB1 0.765 TMEM62_SPCS2_L 0.645 0.861 0.035 0 HLA-DPB1 0.765 PLEKHF2 0.822 0.905 0.005 0.028 HLA-DPB1 0.765 AIF1 0.816 0.915 0.001 0.027 HLA-DPB1 0.765 CALM2_C2orf61 0.733 0.862 0.024 0.004 VAMP2 0.673 JKAMP 0.93 0.977 0 0.023 VAMP2 0.673 OLFM4 0.806 0.837 0.04 0.027 VAMP2 0.673 TCN1 0.816 0.875 0.004 0.021 VAMP2 0.673 WSB2 0.911 0.978 0 0.016 VAMP2 0.673 PLAC8 0.767 0.857 0.001 0.019 VAMP2 0.673 CD63 0.757 0.915 0 0.003 VAMP2 0.673 PDGFC 0.837 0.913 0.001 0.037 VAMP2 0.673 HPGD 0.871 0.921 0.001 0.046 VAMP2 0.673 LRRC70_IPO11 0.923 0.954 0 0.039 VAMP2 0.673 BPI 0.83 0.876 0.002 0.032 VAMP2 0.673 PPP2R5A_SNORA16 0.775 0.854 0.004 0.022 VAMP2 0.673 TMTC1 0.826 0.859 0.01 0.038 VAMP2 0.673 SEC24A_SAR1B 0.761 0.865 0 0.022 VAMP2 0.673 HMGB2 0.849 0.921 0.001 0.005 VAMP2 0.673 EXOSC4 0.731 0.851 0.003 0.009 VAMP2 0.673 FOLR3_FOLR2 0.848 0.929 0 0.002 VAMP2 0.673 PRR13_PCBP2 0.77 0.861 0.004 0.02 VAMP2 0.673 LGALS1 0.736 0.833 0.006 0.047 VAMP2 0.673 FFAR2 0.868 0.926 0 0.011 VAMP2 0.673 MTHFS 0.757 0.885 0.001 0.002 VAMP2 0.673 SIAE 0.814 0.919 0 0.026 VAMP2 0.673 ABCA13 0.868 0.916 0 0.036 VAMP2 0.673 CDA 0.804 0.907 0 0.007 VAMP2 0.673 MTRR 0.703 0.807 0.022 0.007 VAMP2 0.673 AIG1 0.842 0.924 0 0.012 VAMP2 0.673 PCOLCE2 0.709 0.803 0.008 0.022 VAMP2 0.673 GAB2 0.729 0.823 0.021 0.042 VAMP2 0.673 HS2ST1_UBA2 0.758 0.892 0 0.007 VAMP2 0.673 VOPP1_LOC100128 0.618 0.806 0.033 0.004 VAMP2 0.673 SLC11A1 0.785 0.892 0.001 0.019 VAMP2 0.673 ABCA1 0.866 0.922 0 0.018 VAMP2 0.673 GSTO1 0.793 0.891 0 0.019 VAMP2 0.673 CLEC4A 0.87 0.932 0 0.003 VAMP2 0.673 NEK6_LOC1001290 0.765 0.892 0 0.011 VAMP2 0.673 MACF1 0.742 0.837 0.02 0.002 VAMP2 0.673 GALNT2 0.651 0.802 0.01 0.026 VAMP2 0.673 OR9A2 0.813 0.871 0.004 0.032 VAMP2 0.673 DPH3 0.86 0.92 0 0.017 VAMP2 0.673 TRIM21 0.874 0.944 0 0.042 VAMP2 0.673 ICAM1 0.821 0.916 0 0.024 VAMP2 0.673 TAF13 0.854 0.914 0 0.005 VAMP2 0.673 P4HA1_RPL17 0.715 0.819 0.033 0.001 VAMP2 0.673 KLHL5 0.826 0.91 0 0.004 VAMP2 0.673 ANKRD28 0.8 0.906 0 0.02 VAMP2 0.673 LAIR1_LAIR2 0.749 0.878 0.002 0.002 VAMP2 0.673 UBE2F_C20orf194 0.762 0.863 0.007 0.002 VAMP2 0.673 KDM6B_TMEM88 0.711 0.852 0.003 0.014 VAMP2 0.673 DYNLL1 0.776 0.829 0.024 0.028 VAMP2 0.673 FAM118B 0.737 0.828 0.02 0.031 VAMP2 0.673 PLEKHF2 0.822 0.897 0.001 0.008 VAMP2 0.673 AIF1 0.816 0.888 0.001 0.016 VAMP2 0.673 CALM2_C2orf61 0.733 0.838 0.019 0.002 IFI16 0.912 CASS4 0.877 0.989 0.005 0.001 IFI16 0.912 ITK 0.899 0.99 0.006 0.002 IFI16 0.912 GOT2 0.905 0.974 0.049 0.003 IFI16 0.912 CCR3 0.905 0.97 0.037 0.005 IFI16 0.912 CHI3L1 0.909 0.974 0.034 0.017 IFI16 0.912 GIMAP7 0.755 0.979 0.005 0 IFI16 0.912 RUNX2 0.848 0.963 0.017 0.002 IFI16 0.912 HLA-DRA 0.768 0.976 0.012 0 IFI16 0.912 NPCDR1 0.86 0.97 0.038 0 IFI16 0.912 TSHZ2 0.801 0.962 0.046 0 JKAMP 0.93 MRPL41 0.811 0.981 0.041 0 JKAMP 0.93 CASS4 0.877 0.988 0.016 0.001 JKAMP 0.93 ITK 0.899 0.987 0.026 0.002 JKAMP 0.93 GOT2 0.905 0.986 0.021 0.007 JKAMP 0.93 B3GAT3 0.783 0.985 0.008 0 JKAMP 0.93 HIST1H4E 0.83 0.999 0.009 0 JKAMP 0.93 ANAPC11 0.818 0.979 0.022 0 JKAMP 0.93 RUNX2 0.848 0.979 0.023 0.001 JKAMP 0.93 PDE3B 0.85 0.999 0.009 0 MRPL41 0.811 TCN1 0.816 0.933 0.014 0.002 MRPL41 0.811 CD63 0.757 0.958 0 0.001 MRPL41 0.811 BPI 0.83 0.924 0.024 0.003 MRPL41 0.811 HSPC159 0.823 0.922 0.001 0.035 MRPL41 0.811 PPP2R5A_SNORA16 0.775 0.927 0.001 0.004 MRPL41 0.811 TMTC1 0.826 0.9 0.042 0.014 MRPL41 0.811 SEC24A_SAR1B 0.761 0.926 0 0.004 MRPL41 0.811 SH3PXD2B 0.734 0.866 0.026 0.016 MRPL41 0.811 HMGB2 0.849 0.984 0 0.003 MRPL41 0.811 EXOSC4 0.731 0.916 0.001 0.004 MRPL41 0.811 INSIG1 0.509 0.872 0.012 0 MRPL41 0.811 FOLR3_FOLR2 0.848 0.962 0 0.003 MRPL41 0.811 PRR13_PCBP2 0.77 0.889 0.017 0.024 MRPL41 0.811 LGALS1 0.736 0.916 0 0.007 MRPL41 0.811 MTHFS 0.757 0.923 0.001 0.006 MRPL41 0.811 EIF2AK2 0.857 0.935 0.002 0.04 MRPL41 0.811 SIAE 0.814 0.935 0 0.024 MRPL41 0.811 ABCA13 0.868 0.962 0.001 0.001 MRPL41 0.811 CDA 0.804 0.938 0 0.011 MRPL41 0.811 SAP30 0.728 0.853 0.023 0.045 MRPL41 0.811 SUCNR1 0.922 0.986 0 0.012 MRPL41 0.811 AIG1 0.842 0.97 0 0.002 MRPL41 0.811 HS2ST1_UBA2 0.758 0.908 0 0.015 MRPL41 0.811 C22orf37 0.582 0.88 0.018 0 MRPL41 0.811 VOPP1_LOC100128 0.618 0.892 0.003 0 MRPL41 0.811 SLC39A8 0.698 0.898 0.007 0.002 MRPL41 0.811 SLC11A1 0.785 0.915 0 0.033 MRPL41 0.811 TREML1 0.793 0.918 0.006 0.013 MRPL41 0.811 GSTO1 0.793 0.949 0.001 0.003 MRPL41 0.811 CLEC4A 0.87 0.952 0.001 0.028 MRPL41 0.811 NEK6_LOC1001290 0.765 0.906 0.001 0.021 MRPL41 0.811 MACF1 0.742 0.907 0.016 0.001 MRPL41 0.811 AMFR 0.69 0.887 0.002 0.01 MRPL41 0.811 OR9A2 0.813 0.908 0.01 0.016 MRPL41 0.811 HDHD1A 0.63 0.901 0.01 0 MRPL41 0.811 ACPL2 0.651 0.898 0.026 0 MRPL41 0.811 DPH3 0.86 0.958 0 0.014 MRPL41 0.811 TAF13 0.854 0.958 0.003 0.001 MRPL41 0.811 P4HA1_RPL17 0.715 0.897 0.046 0 MRPL41 0.811 KLHL5 0.826 0.92 0.002 0.038 MRPL41 0.811 ANKRD28 0.8 0.938 0 0.01 MRPL41 0.811 MGST3 0.602 0.916 0.005 0 MRPL41 0.811 HSPB1_HSPBL2 0.778 0.884 0.008 0.04 MRPL41 0.811 LHFP 0.759 0.86 0.019 0.045 MRPL41 0.811 LAIR1_LAIR2 0.749 0.904 0.014 0.004 MRPL41 0.811 PTGS1 0.68 0.889 0.012 0.001 MRPL41 0.811 UBE2F_C20orf194 0.762 0.958 0 0 MRPL41 0.811 SRXN1 0.612 0.89 0.028 0 MRPL41 0.811 KDM6B_TMEM88 0.711 0.86 0.032 0.019 MRPL41 0.811 MYL9 0.771 0.916 0.001 0.011 MRPL41 0.811 HIST1H2BJ 0.697 0.905 0.031 0 MRPL41 0.811 CEP97 0.695 0.933 0.002 0 MRPL41 0.811 DYNLL1 0.776 0.95 0.002 0 MRPL41 0.811 FAM118B 0.737 0.914 0.004 0.002 MRPL41 0.811 CTSL1_CTSLL3 0.715 0.919 0.016 0 MRPL41 0.811 NP 0.633 0.888 0.015 0 MRPL41 0.811 PLEKHA3 0.658 0.906 0.005 0 MRPL41 0.811 TMEM62_SPCS2_L 0.645 0.895 0.019 0 MRPL41 0.811 PLEKHF2 0.822 0.934 0.004 0.006 MRPL41 0.811 CMTM5 0.694 0.889 0.019 0.001 MRPL41 0.811 AIF1 0.816 0.931 0.001 0.018 MRPL41 0.811 CDC26 0.619 0.904 0.004 0 MRPL41 0.811 CALM2_C2orf61 0.733 0.959 0 0 OLFM4 0.806 KLRD1 0.784 0.901 0.005 0.024 OLFM4 0.806 HIST1H3J 0.722 0.883 0.029 0.002 OLFM4 0.806 HIST1H3A 0.715 0.887 0.006 0.005 OLFM4 0.806 PMS2CL_PMS2 0.685 0.852 0.028 0.012 OLFM4 0.806 NA 0.763 0.877 0.009 0.035 OLFM4 0.806 B4GALT3 0.659 0.859 0.008 0.012 OLFM4 0.806 CFD 0.767 0.889 0.012 0.027 OLFM4 0.806 IGL@_IGLV1-44_(—) 0.657 0.846 0.043 0.027 CASS4 0.877 WSB2 0.911 0.984 0.001 0.005 CASS4 0.877 CLU 0.894 0.97 0.001 0.007 CASS4 0.877 HPSE 0.939 0.997 0 0.009 CASS4 0.877 C1orf161 0.888 0.963 0.016 0.009 CASS4 0.877 HPGD 0.871 0.968 0.018 0.01 CASS4 0.877 LRRC70_IPO11 0.923 0.974 0.002 0.021 CASS4 0.877 TMEM144_LOC2855 0.92 0.966 0.023 0.017 CASS4 0.877 CDS2 0.869 0.977 0.001 0.006 CASS4 0.877 OLAH 0.885 0.97 0.006 0.005 CASS4 0.877 PPP2R5A_SNORA16 0.775 0.96 0.016 0 CASS4 0.877 EAF2_HCG11_LOC 0.917 0.97 0.011 0.013 CASS4 0.877 RCBTB2_LOC10013 0.905 0.989 0.001 0.004 CASS4 0.877 SEC24A_SAR1B 0.761 0.95 0.018 0 CASS4 0.877 HMGB2 0.849 0.963 0.02 0.003 CASS4 0.877 NA 0.866 0.977 0.008 0.001 CASS4 0.877 PRR13_PCBP2 0.77 0.967 0.012 0 CASS4 0.877 CCR1 0.897 0.977 0.001 0.011 CASS4 0.877 FFAR2 0.868 0.978 0.003 0.001 CASS4 0.877 MTHFS 0.757 0.979 0.003 0 CASS4 0.877 EIF2AK2 0.857 0.95 0.04 0.004 CASS4 0.877 SIAE 0.814 0.952 0.006 0.001 CASS4 0.877 ABCA13 0.868 0.967 0.013 0.001 CASS4 0.877 CDA 0.804 0.961 0.017 0 CASS4 0.877 AGTRAP 0.856 0.937 0.024 0.012 CASS4 0.877 SUCNR1 0.922 0.969 0.004 0.021 CASS4 0.877 AIG1 0.842 0.961 0.014 0.001 CASS4 0.877 GAB2 0.729 0.981 0.002 0 CASS4 0.877 HS2ST1_UBA2 0.758 0.943 0.027 0 CASS4 0.877 SLC11A1 0.785 0.969 0.003 0.001 CASS4 0.877 ABCA1 0.866 0.963 0.023 0.002 CASS4 0.877 CLEC4A 0.87 0.963 0.028 0.003 CASS4 0.877 NEK6_LOC1001290 0.765 0.965 0.002 0 CASS4 0.877 GPR65 0.886 0.952 0.044 0.007 CASS4 0.877 ACTA2 0.783 0.943 0.041 0.001 CASS4 0.877 KCNMA1 0.897 0.959 0.006 0.042 CASS4 0.877 DPH3 0.86 0.967 0.013 0.001 CASS4 0.877 TRIM21 0.874 0.993 0.001 0.003 CASS4 0.877 ICAM1 0.821 0.959 0.004 0.002 CASS4 0.877 KLHL5 0.826 0.955 0.042 0 CASS4 0.877 DLEU2_DLEU2L 0.874 0.969 0.005 0.001 CASS4 0.877 ANKRD28 0.8 0.97 0.007 0 CASS4 0.877 IL1B 0.871 0.98 0.002 0.005 CASS4 0.877 KDM6B_TMEM88 0.711 0.959 0.004 0 CASS4 0.877 ATP6V0D1_LOC100 0.856 0.957 0.001 0.025 CASS4 0.877 AIF1 0.816 0.956 0.043 0.001 TCN1 0.816 KPNA5 0.705 0.881 0.043 0.002 TCN1 0.816 KLRK1_KLRC4 0.857 0.943 0.003 0.025 TCN1 0.816 B3GAT3 0.783 0.896 0.004 0.042 TCN1 0.816 FGFBP2 0.812 0.937 0.006 0.004 TCN1 0.816 KLRD1 0.784 0.918 0.02 0.003 TCN1 0.816 CHI3L1 0.909 0.985 0 0.024 TCN1 0.816 GIMAP7 0.755 0.88 0.027 0.022 TCN1 0.816 DNAJC9_FAM149B1 0.811 0.94 0.004 0.006 TCN1 0.816 PLA2G7 0.834 0.914 0.011 0.043 TCN1 0.816 HSP90AB1_HSP90A 0.799 0.91 0.011 0.043 TCN1 0.816 NA 0.763 0.903 0.014 0.003 TCN1 0.816 C15orf54 0.821 0.914 0.017 0.014 TCN1 0.816 CCR4 0.784 0.915 0.01 0.007 TCN1 0.816 FAM118A 0.759 0.893 0.017 0.016 TCN1 0.816 PYHIN1 0.807 0.914 0.007 0.032 TCN1 0.816 CLC 0.81 0.939 0.002 0.017 TCN1 0.816 B4GALT3 0.659 0.88 0.017 0.002 TCN1 0.816 RASA4_RASA4P_R 0.779 0.901 0.003 0.015 TCN1 0.816 CAMK1D 0.702 0.865 0.02 0.012 TCN1 0.816 IL5RA 0.832 0.917 0.004 0.047 TCN1 0.816 C1orf128 0.751 0.896 0.047 0.004 TCN1 0.816 IGL@_IGLV1-44_(—) 0.657 0.871 0.04 0.007 WSB2 0.911 ITK 0.899 0.987 0.007 0.002 WSB2 0.911 GOT2 0.905 0.993 0.005 0.008 WSB2 0.911 B3GAT3 0.783 0.979 0.022 0 WSB2 0.911 HIST1H4E 0.83 0.968 0.039 0 WSB2 0.911 CCR3 0.905 0.968 0.045 0.003 WSB2 0.911 CHI3L1 0.909 0.981 0.019 0.024 WSB2 0.911 RUNX2 0.848 0.977 0.017 0 WSB2 0.911 HLA-DRA 0.768 0.968 0.049 0 CLU 0.894 ITK 0.899 0.956 0.042 0.004 CLU 0.894 GOT2 0.905 0.968 0.03 0.004 CLU 0.894 HIST1H4E 0.83 0.961 0.016 0.001 CLU 0.894 CCR3 0.905 0.966 0.022 0.005 CLU 0.894 CHI3L1 0.909 0.985 0.006 0.019 CLU 0.894 RUNX2 0.848 0.97 0.005 0.003 CLU 0.894 PHOSPHO1 0.85 0.984 0.004 0.002 CLU 0.894 SULF2 0.819 0.966 0.016 0.001 CLU 0.894 LASS4 0.794 0.959 0.002 0.004 CLU 0.894 TSHZ2 0.801 0.954 0.045 0 KPNA5 0.705 PPP2R5A_SNORA16 0.775 0.888 0 0.036 KPNA5 0.705 NA 0.866 0.934 0 0.033 KPNA5 0.705 MTRR 0.703 0.814 0.013 0.049 KPNA5 0.705 HDHD1A 0.63 0.816 0.009 0.006 KPNA5 0.705 LRRFIP1 0.673 0.802 0.043 0.009 KPNA5 0.705 DPH3 0.86 0.935 0 0.045 KPNA5 0.705 TAF13 0.854 0.943 0 0.021 KPNA5 0.705 P4HA1_RPL17 0.715 0.866 0.002 0.003 KPNA5 0.705 DLEU2_DLEU2L 0.874 0.956 0 0.017 KPNA5 0.705 MGST3 0.602 0.805 0.006 0.01 KPNA5 0.705 FSD1L_GARNL1 0.66 0.829 0.019 0.006 KPNA5 0.705 NA 0.705 0.867 0.01 0 KPNA5 0.705 UBE2F_C20orf194 0.762 0.879 0 0.023 KPNA5 0.705 CEP97 0.695 0.841 0.005 0.017 KPNA5 0.705 DYNLL1 0.776 0.896 0.001 0.001 KPNA5 0.705 FAM118B 0.737 0.842 0.004 0.047 KPNA5 0.705 PLEKHA3 0.658 0.873 0 0.001 KPNA5 0.705 TMEM62_SPCS2_L 0.645 0.843 0 0.007 KPNA5 0.705 PLEKHF2 0.822 0.961 0 0.002 KPNA5 0.705 CMTM5 0.694 0.797 0.036 0.044 KPNA5 0.705 CDC26 0.619 0.786 0.025 0.018 KPNA5 0.705 CALM2_C2orf61 0.733 0.913 0 0 PLAC8 0.767 ITK 0.899 0.971 0 0.005 PLAC8 0.767 B3GAT3 0.783 0.901 0.003 0.036 PLAC8 0.767 GIMAP7 0.755 0.877 0.021 0.008 PLAC8 0.767 HLA-DRA 0.768 0.891 0.015 0.021 PLAC8 0.767 PLA2G7 0.834 0.9 0.024 0.01 PLAC8 0.767 C15orf54 0.821 0.887 0.007 0.034 PLAC8 0.767 LY6G5B_CSNK2B 0.661 0.851 0.037 0.001 PLAC8 0.767 CLC 0.81 0.89 0.026 0.02 CD63 0.757 GOT2 0.905 0.962 0 0.041 CD63 0.757 B3GAT3 0.783 0.961 0 0 CD63 0.757 HIST1H4E 0.83 0.957 0.001 0.001 CD63 0.757 FGFBP2 0.812 0.879 0.045 0.006 CD63 0.757 ANAPC11 0.818 0.925 0.003 0.004 CD63 0.757 RUNX2 0.848 0.921 0.002 0.029 CD63 0.757 PDE3B 0.85 0.917 0.008 0.016 CD63 0.757 JUP 0.804 0.885 0.013 0.039 CD63 0.757 NPCDR1 0.86 0.905 0.004 0.043 CD63 0.757 C15orf54 0.821 0.879 0.048 0.009 CD63 0.757 CCR4 0.784 0.879 0.022 0.009 CD63 0.757 LASS4 0.794 0.92 0.003 0.016 CD63 0.757 TSHZ2 0.801 0.883 0.025 0.015 CD63 0.757 RASA4_RASA4P_R 0.779 0.861 0.027 0.016 HPSE 0.939 ITK 0.899 0.997 0.008 0.002 HPSE 0.939 GOT2 0.905 0.996 0.011 0.006 HPSE 0.939 HIST1H4E 0.83 0.979 0.029 0 HPSE 0.939 CHI3L1 0.909 0.998 0.009 0.016 HPSE 0.939 GIMAP7 0.755 0.994 0.01 0 HPSE 0.939 RUNX2 0.848 0.984 0.025 0 HPSE 0.939 LY6G5B_CSNK2B 0.661 0.985 0.028 0 HPSE 0.939 CCR4 0.784 0.992 0.015 0 HPSE 0.939 PYHIN1 0.807 0.989 0.029 0 C1orf161 0.888 CCR3 0.905 0.954 0.038 0.038 C1orf161 0.888 RUNX2 0.848 0.967 0.02 0.005 C1orf161 0.888 PHOSPHO1 0.85 0.954 0.035 0.017 C1orf161 0.888 PDE3B 0.85 0.959 0.019 0.011 C1orf161 0.888 ABCG1 0.789 0.95 0.008 0.008 KLRK1_KLRC4 0.857 ATP13A3 0.841 0.965 0.001 0.022 KLRK1_KLRC4 0.857 HSPC159 0.823 0.94 0.008 0.007 KLRK1_KLRC4 0.857 PPP2R5A_SNORA16 0.775 0.947 0.002 0.003 KLRK1_KLRC4 0.857 TMTC1 0.826 0.934 0.012 0.013 KLRK1_KLRC4 0.857 HMGB2 0.849 0.951 0.007 0.024 KLRK1_KLRC4 0.857 NA 0.866 0.95 0.003 0.034 KLRK1_KLRC4 0.857 FOLR3_FOLR2 0.848 0.942 0.017 0.009 KLRK1_KLRC4 0.857 LGALS1 0.736 0.905 0.02 0.009 KLRK1_KLRC4 0.857 MTHFS 0.757 0.907 0.021 0.007 KLRK1_KLRC4 0.857 EIF2AK2 0.857 0.946 0.018 0.009 KLRK1_KLRC4 0.857 SIAE 0.814 0.932 0.004 0.017 KLRK1_KLRC4 0.857 CDA 0.804 0.917 0.033 0.015 KLRK1_KLRC4 0.857 SAP30 0.728 0.89 0.009 0.011 KLRK1_KLRC4 0.857 AIG1 0.842 0.93 0.021 0.04 KLRK1_KLRC4 0.857 HS2ST1_UBA2 0.758 0.902 0.017 0.017 KLRK1_KLRC4 0.857 SLC11A1 0.785 0.896 0.022 0.039 KLRK1_KLRC4 0.857 TREML1 0.793 0.919 0.038 0.011 KLRK1_KLRC4 0.857 GSTO1 0.793 0.934 0.004 0.017 KLRK1_KLRC4 0.857 NEK6_LOC1001290 0.765 0.886 0.025 0.039 KLRK1_KLRC4 0.857 DPH3 0.86 0.942 0.002 0.034 KLRK1_KLRC4 0.857 TAF13 0.854 0.953 0.015 0.016 KLRK1_KLRC4 0.857 KLHL5 0.826 0.921 0.015 0.043 KLRK1_KLRC4 0.857 DLEU2_DLEU2L 0.874 0.954 0.003 0.023 KLRK1_KLRC4 0.857 ANKRD28 0.8 0.945 0.005 0.006 KLRK1_KLRC4 0.857 HSPB1_HSPBL2 0.778 0.918 0.012 0.006 KLRK1_KLRC4 0.857 LHFP 0.759 0.903 0.012 0.007 KLRK1_KLRC4 0.857 ZRANB1 0.724 0.923 0.014 0 KLRK1_KLRC4 0.857 UBE2F_C20orf194 0.762 0.916 0.013 0.002 KLRK1_KLRC4 0.857 MYL9 0.771 0.912 0.023 0.011 KLRK1_KLRC4 0.857 CEP97 0.695 0.929 0.031 0 KLRK1_KLRC4 0.857 DYNLL1 0.776 0.95 0.017 0 KLRK1_KLRC4 0.857 FAM118B 0.737 0.933 0.024 0 KLRK1_KLRC4 0.857 PLEKHA3 0.658 0.913 0.014 0 KLRK1_KLRC4 0.857 TMEM62_SPCS2_L 0.645 0.94 0.007 0 KLRK1_KLRC4 0.857 PLEKHF2 0.822 0.939 0.005 0.015 KLRK1_KLRC4 0.857 CALM2_C2orf61 0.733 0.914 0.01 0.001 ATP13A3 0.841 ITK 0.899 0.978 0.004 0.044 ATP13A3 0.841 B3GAT3 0.783 0.94 0.018 0.002 ATP13A3 0.841 HIST1H4E 0.83 0.937 0.009 0.03 ATP13A3 0.841 GIMAP7 0.755 0.92 0.026 0.009 ATP13A3 0.841 ANAPC11 0.818 0.941 0.012 0.01 ATP13A3 0.841 RUNX2 0.848 0.945 0.007 0.032 ATP13A3 0.841 PDE3B 0.85 0.961 0.01 0.017 ATP13A3 0.841 NPCDR1 0.86 0.937 0.024 0.026 ATP13A3 0.841 CCR4 0.784 0.947 0.01 0.002 ATP13A3 0.841 ABCG1 0.789 0.909 0.017 0.039 ATP13A3 0.841 PYHIN1 0.807 0.941 0.021 0.006 ATP13A3 0.841 TSHZ2 0.801 0.94 0.004 0.016 ATP13A3 0.841 RASA4_RASA4P_R 0.779 0.903 0.039 0.02 ITK 0.899 PDGFC 0.837 0.961 0.01 0.011 ITK 0.899 HPGD 0.871 0.955 0.033 0.034 ITK 0.899 TMEM144_LOC2855 0.92 0.983 0.005 0.039 ITK 0.899 PPP2R5A_SNORA16 0.775 0.977 0.006 0 ITK 0.899 EAF2_HCG11_LOC 0.917 0.992 0.004 0.021 ITK 0.899 RCBTB2_LOC10013 0.905 0.973 0.011 0.024 ITK 0.899 SEC24A_SAR1B 0.761 0.977 0.005 0 ITK 0.899 HMGB2 0.849 0.976 0.008 0.004 ITK 0.899 SLC39A9 0.551 0.969 0.006 0 ITK 0.899 NA 0.866 0.978 0.009 0.002 ITK 0.899 FFAR2 0.868 0.957 0.021 0.004 ITK 0.899 MTHFS 0.757 0.962 0.009 0 ITK 0.899 EIF2AK2 0.857 0.98 0.009 0.001 ITK 0.899 SIAE 0.814 0.961 0.006 0.001 ITK 0.899 ABCA13 0.868 0.953 0.046 0.004 ITK 0.899 CDA 0.804 0.96 0.015 0.001 ITK 0.899 SUCNR1 0.922 0.976 0.004 0.03 ITK 0.899 MTRR 0.703 0.993 0.002 0 ITK 0.899 AIG1 0.842 0.976 0.004 0.001 ITK 0.899 HS2ST1_UBA2 0.758 0.948 0.01 0.001 ITK 0.899 GSTO1 0.793 0.966 0.019 0 ITK 0.899 CLEC4A 0.87 0.978 0.008 0.003 ITK 0.899 TRAF3 0.594 0.966 0.007 0 ITK 0.899 GPR65 0.886 0.974 0.004 0.02 ITK 0.899 MACF1 0.742 0.978 0.023 0 ITK 0.899 GALNT2 0.651 0.932 0.045 0 ITK 0.899 HDHD1A 0.63 0.961 0.016 0 ITK 0.899 ACTA2 0.783 0.953 0.015 0.001 ITK 0.899 LRRFIP1 0.673 0.981 0.009 0 ITK 0.899 SFRS9 0.527 0.959 0.021 0 ITK 0.899 DPH3 0.86 0.961 0.021 0.003 ITK 0.899 TRIM21 0.874 0.985 0.002 0.005 ITK 0.899 ICAM1 0.821 0.944 0.015 0.007 ITK 0.899 TAF13 0.854 0.974 0.01 0.002 ITK 0.899 P4HA1_RPL17 0.715 0.984 0.007 0 ITK 0.899 KLHL5 0.826 0.984 0.003 0 ITK 0.899 DLEU2_DLEU2L 0.874 0.959 0.033 0.003 ITK 0.899 ANKRD28 0.8 0.975 0.011 0 ITK 0.899 CCDC125 0.632 0.968 0.018 0 ITK 0.899 LAIR1_LAIR2 0.749 0.979 0.006 0 ITK 0.899 CEP97 0.695 0.967 0.015 0 ITK 0.899 DYNLL1 0.776 0.97 0.035 0 ITK 0.899 PLIN2 0.671 0.971 0.009 0 ITK 0.899 FAM118B 0.737 0.989 0.003 0 ITK 0.899 NP 0.633 0.956 0.041 0 ITK 0.899 TMEM62_SPCS2_L 0.645 0.977 0.011 0 ITK 0.899 PLEKHF2 0.822 0.958 0.042 0.001 ITK 0.899 AIF1 0.816 0.961 0.022 0.001 ITK 0.899 DCTN5 0.557 0.988 0.004 0 PMAIP1 0.622 NA 0.705 0.808 0.009 0.026 GOT2 0.905 LRRC70_IPO11 0.923 0.972 0.024 0.042 GOT2 0.905 PPP2R5A_SNORA16 0.775 0.96 0.048 0 GOT2 0.905 EAF2_HCG11_LOC 0.917 0.978 0.018 0.04 GOT2 0.905 RCBTB2_LOC10013 0.905 0.968 0.007 0.037 GOT2 0.905 SEC24A_SAR1B 0.761 0.965 0.014 0 GOT2 0.905 FOLR3_FOLR2 0.848 0.964 0.046 0.002 GOT2 0.905 CCR1 0.897 0.963 0.024 0.024 GOT2 0.905 FFAR2 0.868 0.961 0.02 0.007 GOT2 0.905 EIF2AK2 0.857 0.975 0.021 0.003 GOT2 0.905 SIAE 0.814 0.967 0.013 0.001 GOT2 0.905 AGTRAP 0.856 0.949 0.04 0.023 GOT2 0.905 SUCNR1 0.922 0.992 0.011 0.008 GOT2 0.905 AIG1 0.842 0.979 0.028 0.001 GOT2 0.905 GSTO1 0.793 0.985 0.012 0 GOT2 0.905 CLEC4A 0.87 0.978 0.009 0.006 GOT2 0.905 ACTA2 0.783 0.948 0.032 0.002 GOT2 0.905 TRIM21 0.874 0.989 0.005 0.005 GOT2 0.905 ICAM1 0.821 0.956 0.018 0.007 GOT2 0.905 TAF13 0.854 0.972 0.049 0.001 GOT2 0.905 KLHL5 0.826 0.961 0.024 0.002 GOT2 0.905 DLEU2_DLEU2L 0.874 0.948 0.037 0.018 GOT2 0.905 ANKRD28 0.8 0.967 0.038 0.001 GOT2 0.905 LAIR1_LAIR2 0.749 0.968 0.041 0 GOT2 0.905 AIF1 0.816 0.958 0.02 0.004 GOT2 0.905 CALM2_C2orf61 0.733 0.961 0.047 0 PDGFC 0.837 B3GAT3 0.783 0.958 0.01 0 PDGFC 0.837 HIST1H4E 0.83 0.948 0.02 0.002 PDGFC 0.837 RUNX2 0.848 0.966 0.004 0.008 PDGFC 0.837 PMS2CL_PMS2 0.685 0.924 0.027 0 PDGFC 0.837 PDE3B 0.85 0.945 0.01 0.034 PDGFC 0.837 NPCDR1 0.86 0.925 0.035 0.041 PDGFC 0.837 CCR4 0.784 0.935 0.028 0.001 PDGFC 0.837 FAM118A 0.759 0.907 0.05 0.009 PDGFC 0.837 TSHZ2 0.801 0.929 0.026 0.009 B3GAT3 0.783 HPGD 0.871 0.945 0 0.047 B3GAT3 0.783 LRRC70_IPO11 0.923 0.96 0 0.04 B3GAT3 0.783 BPI 0.83 0.901 0.031 0.004 B3GAT3 0.783 PPP2R5A_SNORA16 0.775 0.9 0.011 0.006 B3GAT3 0.783 SEC24A_SAR1B 0.761 0.944 0.001 0 B3GAT3 0.783 HMGB2 0.849 0.967 0 0.004 B3GAT3 0.783 EXOSC4 0.731 0.93 0 0.001 B3GAT3 0.783 FOLR3_FOLR2 0.848 0.957 0 0.003 B3GAT3 0.783 PRR13_PCBP2 0.77 0.889 0.029 0.006 B3GAT3 0.783 LGALS1 0.736 0.912 0.002 0.003 B3GAT3 0.783 FFAR2 0.868 0.95 0 0.008 B3GAT3 0.783 MTHFS 0.757 0.926 0.001 0.001 B3GAT3 0.783 EIF2AK2 0.857 0.914 0.013 0.013 B3GAT3 0.783 SIAE 0.814 0.947 0 0.006 B3GAT3 0.783 ABCA13 0.868 0.934 0.002 0.02 B3GAT3 0.783 CDA 0.804 0.942 0 0.003 B3GAT3 0.783 AGTRAP 0.856 0.952 0 0.025 B3GAT3 0.783 AIG1 0.842 0.963 0 0.001 B3GAT3 0.783 PCOLCE2 0.709 0.872 0.033 0.001 B3GAT3 0.783 HS2ST1_UBA2 0.758 0.92 0.001 0.003 B3GAT3 0.783 VOPP1_LOC100128 0.618 0.865 0.028 0 B3GAT3 0.783 SLC11A1 0.785 0.894 0.003 0.049 B3GAT3 0.783 GSTO1 0.793 0.934 0.002 0.001 B3GAT3 0.783 CLEC4A 0.87 0.952 0.001 0.006 B3GAT3 0.783 NEK6_LOC1001290 0.765 0.906 0.002 0.01 B3GAT3 0.783 GALNT2 0.651 0.889 0.012 0 B3GAT3 0.783 GLT25D1 0.607 0.876 0.035 0 B3GAT3 0.783 OR9A2 0.813 0.882 0.029 0.03 B3GAT3 0.783 ACTA2 0.783 0.897 0.024 0.008 B3GAT3 0.783 DPH3 0.86 0.944 0.001 0.014 B3GAT3 0.783 TRIM21 0.874 0.968 0 0.012 B3GAT3 0.783 ICAM1 0.821 0.94 0 0.012 B3GAT3 0.783 TAF13 0.854 0.944 0.005 0.001 B3GAT3 0.783 KLHL5 0.826 0.923 0.003 0.009 B3GAT3 0.783 ANKRD28 0.8 0.93 0.002 0.004 B3GAT3 0.783 IL1B 0.871 0.929 0.003 0.041 B3GAT3 0.783 LAIR1_LAIR2 0.749 0.906 0.014 0.001 B3GAT3 0.783 UBE2F_C20orf194 0.762 0.924 0.011 0 B3GAT3 0.783 CCRL2 0.786 0.881 0.048 0.02 B3GAT3 0.783 KDM6B_TMEM88 0.711 0.857 0.04 0.014 B3GAT3 0.783 MYL9 0.771 0.87 0.014 0.035 B3GAT3 0.783 IRF1 0.651 0.872 0.04 0 B3GAT3 0.783 FAM118B 0.737 0.888 0.046 0.001 B3GAT3 0.783 CTSL1_CTSLL3 0.715 0.916 0.011 0 B3GAT3 0.783 PLEKHA3 0.658 0.869 0.043 0 B3GAT3 0.783 PLEKHF2 0.822 0.917 0.007 0.009 B3GAT3 0.783 AIF1 0.816 0.939 0.001 0.002 B3GAT3 0.783 CALM2_C2orf61 0.733 0.913 0.008 0 HIST1H4E 0.83 HPGD 0.871 0.948 0.003 0.042 HIST1H4E 0.83 LRRC70_IPO11 0.923 0.975 0 0.042 HIST1H4E 0.83 ECHDC3 0.629 0.892 0.042 0 HIST1H4E 0.83 HSPC159 0.823 0.936 0.031 0.001 HIST1H4E 0.83 OLAH 0.885 0.968 0 0.025 HIST1H4E 0.83 PPP2R5A_SNORA16 0.775 0.953 0.005 0 HIST1H4E 0.83 HMGB2 0.849 0.959 0.008 0.003 HIST1H4E 0.83 EXOSC4 0.731 0.957 0 0 HIST1H4E 0.83 NA 0.866 0.926 0.024 0.011 HIST1H4E 0.83 FOLR3_FOLR2 0.848 0.967 0.002 0.001 HIST1H4E 0.83 LGALS1 0.736 0.946 0.002 0 HIST1H4E 0.83 MTHFS 0.757 0.921 0.006 0.001 HIST1H4E 0.83 SIAE 0.814 0.979 0 0 HIST1H4E 0.83 ABCA13 0.868 0.971 0.003 0.001 HIST1H4E 0.83 EFCAB2 0.96 0.987 0 0.04 HIST1H4E 0.83 CDA 0.804 0.955 0.002 0.001 HIST1H4E 0.83 SAP30 0.728 0.902 0.008 0.003 HIST1H4E 0.83 AGTRAP 0.856 0.98 0 0.004 HIST1H4E 0.83 SUCNR1 0.922 0.988 0.001 0.004 HIST1H4E 0.83 AIG1 0.842 0.951 0.01 0.001 HIST1H4E 0.83 PCOLCE2 0.709 0.914 0.018 0 HIST1H4E 0.83 HS2ST1_UBA2 0.758 0.92 0.005 0.002 HIST1H4E 0.83 C22orf37 0.582 0.916 0.011 0 HIST1H4E 0.83 VOPP1_LOC100128 0.618 0.906 0.023 0 HIST1H4E 0.83 SLC39A8 0.698 0.934 0.021 0 HIST1H4E 0.83 SLC11A1 0.785 0.945 0.001 0.004 HIST1H4E 0.83 GSTO1 0.793 0.925 0.023 0.003 HIST1H4E 0.83 CLEC4A 0.87 0.935 0.008 0.03 HIST1H4E 0.83 NEK6_LOC1001290 0.765 0.919 0.001 0.005 HIST1H4E 0.83 AMFR 0.69 0.9 0.034 0.001 HIST1H4E 0.83 OR9A2 0.813 0.914 0.044 0.003 HIST1H4E 0.83 DPH3 0.86 0.965 0.001 0.002 HIST1H4E 0.83 TRIM21 0.874 0.926 0.02 0.044 HIST1H4E 0.83 ICAM1 0.821 0.914 0.014 0.02 HIST1H4E 0.83 TAF13 0.854 0.957 0.018 0.001 HIST1H4E 0.83 KLHL5 0.826 0.916 0.037 0.014 HIST1H4E 0.83 DLEU2_DLEU2L 0.874 0.956 0.002 0.006 HIST1H4E 0.83 ANKRD28 0.8 0.945 0.005 0.002 HIST1H4E 0.83 HSPB1_HSPBL2 0.778 0.921 0.019 0.002 HIST1H4E 0.83 LHFP 0.759 0.897 0.01 0.005 HIST1H4E 0.83 UBE2F_C20orf194 0.762 0.936 0.038 0 HIST1H4E 0.83 MYL9 0.771 0.948 0.003 0.001 HIST1H4E 0.83 PLEKHA3 0.658 0.916 0.036 0 HIST1H4E 0.83 PLEKHF2 0.822 0.941 0.035 0 HIST1H4E 0.83 CMTM5 0.694 0.924 0.041 0 HIST1H4E 0.83 AIF1 0.816 0.915 0.036 0.013 HIST1H4E 0.83 CDC26 0.619 0.908 0.041 0 HIST1H4E 0.83 CALM2_C2orf61 0.733 0.938 0.021 0 HPGD 0.871 RUNX2 0.848 0.95 0.009 0.029 HPGD 0.871 PDE3B 0.85 0.957 0.005 0.04 HPGD 0.871 NPCDR1 0.86 0.94 0.048 0.014 HPGD 0.871 C15orf54 0.821 0.941 0.027 0.005 HPGD 0.871 ABCG1 0.789 0.936 0.02 0.021 HPGD 0.871 LASS4 0.794 0.935 0.033 0.017 HPGD 0.871 TSHZ2 0.801 0.935 0.035 0.01 FGFBP2 0.812 BPI 0.83 0.913 0.018 0.017 FGFBP2 0.812 HSPC159 0.823 0.915 0.003 0.047 FGFBP2 0.812 PPP2R5A_SNORA16 0.775 0.9 0.001 0.03 FGFBP2 0.812 TMTC1 0.826 0.917 0.004 0.038 FGFBP2 0.812 EXOSC4 0.731 0.865 0.032 0.018 FGFBP2 0.812 PCOLCE2 0.709 0.86 0.022 0.009 FGFBP2 0.812 TREML1 0.793 0.914 0.005 0.023 FGFBP2 0.812 AMFR 0.69 0.867 0.006 0.021 FGFBP2 0.812 ANKRD28 0.8 0.913 0.002 0.037 FGFBP2 0.812 HSPB1_HSPBL2 0.778 0.876 0.014 0.049 FGFBP2 0.812 LHFP 0.759 0.871 0.005 0.042 FGFBP2 0.812 ZRANB1 0.724 0.877 0.025 0.007 FGFBP2 0.812 PTGS1 0.68 0.871 0.031 0.002 FGFBP2 0.812 UBE2F_C20orf194 0.762 0.895 0.005 0.01 FGFBP2 0.812 MYL9 0.771 0.905 0.004 0.019 FGFBP2 0.812 HIST1H2BJ 0.697 0.887 0.035 0.002 FGFBP2 0.812 CEP97 0.695 0.882 0.031 0.002 FGFBP2 0.812 DYNLL1 0.776 0.901 0.031 0.002 FGFBP2 0.812 FAM118B 0.737 0.885 0.039 0.007 FGFBP2 0.812 SPARC 0.703 0.889 0.026 0.001 FGFBP2 0.812 PLEKHA3 0.658 0.866 0.034 0.002 FGFBP2 0.812 CMTM5 0.694 0.868 0.047 0.004 FGFBP2 0.812 CALM2_C2orf61 0.733 0.865 0.028 0.019 LRRC70_IPO11 0.923 RUNX2 0.848 0.965 0.022 0.005 LRRC70_IPO11 0.923 PHOSPHO1 0.85 0.965 0.018 0.009 LRRC70_IPO11 0.923 PDE3B 0.85 0.987 0.021 0.001 LRRC70_IPO11 0.923 ABCG1 0.789 0.979 0.015 0.001 TMEM144_LOC2855 0.92 CCR3 0.905 0.986 0.036 0.005 TMEM144_LOC2855 0.92 GIMAP7 0.755 0.99 0.02 0 TMEM144_LOC2855 0.92 RUNX2 0.848 0.963 0.035 0.009 TMEM144_LOC2855 0.92 HLA-DRA 0.768 0.975 0.047 0 TMEM144_LOC2855 0.92 PLA2G7 0.834 0.985 0.047 0.001 TMEM144_LOC2855 0.92 PMS2CL_PMS2 0.685 0.952 0.043 0 TMEM144_LOC2855 0.92 PDE3B 0.85 0.968 0.021 0.011 TMEM144_LOC2855 0.92 CCR4 0.784 0.99 0.014 0 TMEM144_LOC2855 0.92 ABCG1 0.789 0.962 0.021 0.001 TMEM144_LOC2855 0.92 FAM118A 0.759 0.949 0.033 0.001 TMEM144_LOC2855 0.92 TSHZ2 0.801 0.967 0.029 0.001 TMEM144_LOC2855 0.92 CFD 0.767 0.963 0.042 0 CDS2 0.869 CHI3L1 0.909 0.965 0.042 0.01 CDS2 0.869 RUNX2 0.848 0.979 0.005 0.001 BPI 0.83 DNAJC9_FAM149B1 0.811 0.914 0.014 0.022 BPI 0.83 IRS2 0.648 0.879 0.047 0.002 BPI 0.83 NA 0.763 0.897 0.023 0.004 BPI 0.83 CCR4 0.784 0.894 0.029 0.024 BPI 0.83 RRP12_LOC644215 0.632 0.869 0.038 0.001 BPI 0.83 CLC 0.81 0.926 0.006 0.021 BPI 0.83 B4GALT3 0.659 0.898 0.004 0.001 BPI 0.83 RASA4_RASA4P_R 0.779 0.897 0.012 0.017 ECHDC3 0.629 PHOSPHO1 0.85 0.941 0 0.033 ECHDC3 0.629 HIST1H3J 0.722 0.784 0.034 0.012 ECHDC3 0.629 HIST1H3A 0.715 0.796 0.022 0.011 ECHDC3 0.629 IRS2 0.648 0.871 0 0.005 ECHDC3 0.629 S100B 0.72 0.799 0.018 0.005 ECHDC3 0.629 C4orf3 0.629 0.744 0.031 0.049 ECHDC3 0.629 TAS2R31 0.685 0.751 0.028 0.046 CCR3 0.905 RCBTB2_LOC10013 0.905 0.974 0.005 0.02 CCR3 0.905 SEC24A_SAR1B 0.761 0.943 0.024 0 CCR3 0.905 NA 0.866 0.968 0.015 0.004 CCR3 0.905 CCR1 0.897 0.978 0.005 0.015 CCR3 0.905 FFAR2 0.868 0.975 0.005 0.003 CCR3 0.905 EIF2AK2 0.857 0.967 0.022 0.002 CCR3 0.905 SIAE 0.814 0.946 0.029 0.002 CCR3 0.905 AGTRAP 0.856 0.948 0.015 0.021 CCR3 0.905 SUCNR1 0.922 0.978 0.004 0.033 CCR3 0.905 AIG1 0.842 0.954 0.028 0.004 CCR3 0.905 GAB2 0.729 0.948 0.045 0 CCR3 0.905 ABCA1 0.866 0.976 0.012 0.009 CCR3 0.905 GSTO1 0.793 0.956 0.014 0.002 CCR3 0.905 CLEC4A 0.87 0.988 0.003 0.004 CCR3 0.905 GPR65 0.886 0.982 0.004 0.012 CCR3 0.905 KCNMA1 0.897 0.963 0.029 0.019 CCR3 0.905 DPH3 0.86 0.956 0.026 0.003 CCR3 0.905 TRIM21 0.874 0.979 0.004 0.009 CCR3 0.905 ICAM1 0.821 0.976 0.002 0.002 CCR3 0.905 TAF13 0.854 0.967 0.02 0.004 CCR3 0.905 KLHL5 0.826 0.958 0.016 0.001 CCR3 0.905 DLEU2_DLEU2L 0.874 0.974 0.01 0.002 CCR3 0.905 ANKRD28 0.8 0.953 0.02 0.002 CCR3 0.905 IL1B 0.871 0.979 0.003 0.013 CCR3 0.905 UBE2F_C20orf194 0.762 0.949 0.039 0 CCR3 0.905 PLEKHF2 0.822 0.967 0.027 0.001 CCR3 0.905 AIF1 0.816 0.966 0.005 0.002 CCR3 0.905 CALM2_C2orf61 0.733 0.948 0.047 0 HSPC159 0.823 KLRD1 0.784 0.91 0.043 0.001 HSPC159 0.823 CHI3L1 0.909 0.989 0.001 0.027 HSPC159 0.823 ANAPC11 0.818 0.928 0.005 0.012 HSPC159 0.823 RUNX2 0.848 0.963 0 0.016 HSPC159 0.823 PHOSPHO1 0.85 0.943 0.003 0.032 HSPC159 0.823 PDE3B 0.85 0.966 0.001 0.008 HSPC159 0.823 SULF2 0.819 0.929 0.005 0.032 HSPC159 0.823 C15orf54 0.821 0.923 0.041 0.001 HSPC159 0.823 CCR4 0.784 0.925 0.008 0.002 HSPC159 0.823 FAM118A 0.759 0.89 0.019 0.02 HSPC159 0.823 PYHIN1 0.807 0.911 0.011 0.02 HSPC159 0.823 CLC 0.81 0.914 0.039 0.014 HSPC159 0.823 TSHZ2 0.801 0.929 0.001 0.014 HSPC159 0.823 CAMK1D 0.702 0.884 0.037 0.003 OLAH 0.885 CHI3L1 0.909 0.968 0.047 0.018 OLAH 0.885 RUNX2 0.848 0.968 0.012 0.003 OLAH 0.885 PHOSPHO1 0.85 0.96 0.01 0.011 OLAH 0.885 PDE3B 0.85 0.977 0.009 0.002 OLAH 0.885 IRS2 0.648 0.98 0.003 0 OLAH 0.885 ABCG1 0.789 0.96 0.006 0.003 OLAH 0.885 LASS4 0.794 0.967 0.002 0.003 PPP2R5A_SNORA16 0.775 KLRD1 0.784 0.899 0.033 0 PPP2R5A_SNORA16 0.775 GIMAP7 0.755 0.913 0.003 0.001 PPP2R5A_SNORA16 0.775 ANAPC11 0.818 0.915 0.003 0.014 PPP2R5A_SNORA16 0.775 RUNX2 0.848 0.945 0 0.02 PPP2R5A_SNORA16 0.775 HLA-DRA 0.768 0.87 0.014 0.032 PPP2R5A_SNORA16 0.775 DNAJC9_FAM149B1 0.811 0.92 0.027 0.001 PPP2R5A_SNORA16 0.775 PLA2G7 0.834 0.898 0.023 0.015 PPP2R5A_SNORA16 0.775 PMS2CL_PMS2 0.685 0.887 0.014 0 PPP2R5A_SNORA16 0.775 PDE3B 0.85 0.966 0 0.003 PPP2R5A_SNORA16 0.775 SULF2 0.819 0.908 0.004 0.034 PPP2R5A_SNORA16 0.775 IRF4 0.781 0.897 0.012 0.012 PPP2R5A_SNORA16 0.775 HSP90AB1_HSP90A 0.799 0.904 0.003 0.015 PPP2R5A_SNORA16 0.775 ITGA4_CERKL 0.694 0.885 0.03 0.001 PPP2R5A_SNORA16 0.775 NPCDR1 0.86 0.929 0.001 0.016 PPP2R5A_SNORA16 0.775 NA 0.763 0.883 0.028 0.003 PPP2R5A_SNORA16 0.775 C15orf54 0.821 0.888 0.031 0.005 PPP2R5A_SNORA16 0.775 LY6G5B_CSNK2B 0.661 0.887 0.008 0 PPP2R5A_SNORA16 0.775 CCR4 0.784 0.938 0.001 0.001 PPP2R5A_SNORA16 0.775 FAM118A 0.759 0.875 0.015 0.018 PPP2R5A_SNORA16 0.775 PYHIN1 0.807 0.933 0.004 0.001 PPP2R5A_SNORA16 0.775 CLC 0.81 0.905 0.013 0.016 PPP2R5A_SNORA16 0.775 SYNE2 0.678 0.884 0.035 0 PPP2R5A_SNORA16 0.775 TSHZ2 0.801 0.925 0 0.008 PPP2R5A_SNORA16 0.775 RASA4_RASA4P_R 0.779 0.885 0.007 0.01 PPP2R5A_SNORA16 0.775 CAMK1D 0.702 0.887 0.01 0.001 PPP2R5A_SNORA16 0.775 IL5RA 0.832 0.893 0.019 0.034 TMTC1 0.826 ANAPC11 0.818 0.905 0.011 0.047 TMTC1 0.826 PHOSPHO1 0.85 0.937 0.006 0.046 TMTC1 0.826 C15orf54 0.821 0.925 0.006 0.013 TMTC1 0.826 CCR4 0.784 0.893 0.023 0.021 TMTC1 0.826 CFD 0.767 0.901 0.027 0.008 EAF2_HCG11_LOC 0.917 GIMAP7 0.755 0.975 0.04 0 EAF2_HCG11_LOC 0.917 RUNX2 0.848 0.952 0.043 0.012 EAF2_HCG11_LOC 0.917 PDE3B 0.85 0.97 0.05 0.004 EAF2_HCG11_LOC 0.917 ITGA4_CERKL 0.694 0.989 0.035 0 EAF2_HCG11_LOC 0.917 TSHZ2 0.801 0.974 0.028 0 RCBTB2_LOC10013 0.905 CHI3L1 0.909 0.966 0.048 0.029 RCBTB2_LOC10013 0.905 GIMAP7 0.755 0.97 0.008 0 RCBTB2_LOC10013 0.905 RUNX2 0.848 0.98 0.007 0.002 RCBTB2_LOC10013 0.905 CCR4 0.784 0.962 0.05 0 SEC24A_SAR1B 0.761 GIMAP7 0.755 0.915 0.001 0.001 SEC24A_SAR1B 0.761 ANAPC11 0.818 0.895 0.004 0.043 SEC24A_SAR1B 0.761 HLA-DRA 0.768 0.897 0.003 0.005 SEC24A_SAR1B 0.761 DNAJC9_FAM149B1 0.811 0.912 0.022 0.001 SEC24A_SAR1B 0.761 PLA2G7 0.834 0.903 0.014 0.006 SEC24A_SAR1B 0.761 PDE3B 0.85 0.935 0.002 0.017 SEC24A_SAR1B 0.761 SULF2 0.819 0.893 0.007 0.026 SEC24A_SAR1B 0.761 IRF4 0.781 0.88 0.022 0.024 SEC24A_SAR1B 0.761 HSP90AB1_HSP90A 0.799 0.927 0.004 0.001 SEC24A_SAR1B 0.761 ITGA4_CERKL 0.694 0.881 0.024 0.001 SEC24A_SAR1B 0.761 NPCDR1 0.86 0.922 0.001 0.012 SEC24A_SAR1B 0.761 NA 0.763 0.875 0.031 0.001 SEC24A_SAR1B 0.761 C15orf54 0.821 0.886 0.023 0.011 SEC24A_SAR1B 0.761 LY6G5B_CSNK2B 0.661 0.874 0.024 0 SEC24A_SAR1B 0.761 CCR4 0.784 0.903 0.004 0.004 SEC24A_SAR1B 0.761 FAM118A 0.759 0.872 0.039 0.004 SEC24A_SAR1B 0.761 PYHIN1 0.807 0.912 0.006 0.004 SEC24A_SAR1B 0.761 CLC 0.81 0.901 0.013 0.008 SEC24A_SAR1B 0.761 SON 0.604 0.87 0.037 0 SEC24A_SAR1B 0.761 TSHZ2 0.801 0.909 0.002 0.01 SEC24A_SAR1B 0.761 RASA4_RASA4P_R 0.779 0.893 0.005 0.003 SEC24A_SAR1B 0.761 IL5RA 0.832 0.889 0.02 0.016 SH3PXD2B 0.734 NA 0.696 0.817 0.032 0.02 SH3PXD2B 0.734 NA 0.746 0.825 0.047 0.017 SH3PXD2B 0.734 CFD 0.767 0.857 0.029 0.035 HMGB2 0.849 GIMAP7 0.755 0.934 0.011 0.001 HMGB2 0.849 ANAPC11 0.818 0.963 0.008 0.001 HMGB2 0.849 RUNX2 0.848 0.969 0.004 0.004 HMGB2 0.849 DNAJC9_FAM149B1 0.811 0.974 0.011 0 HMGB2 0.849 PMS2CL_PMS2 0.685 0.93 0.008 0 HMGB2 0.849 PDE3B 0.85 0.973 0.003 0.007 HMGB2 0.849 IRF4 0.781 0.932 0.005 0.012 HMGB2 0.849 NPCDR1 0.86 0.933 0.017 0.035 HMGB2 0.849 C15orf54 0.821 0.93 0.039 0.003 HMGB2 0.849 LY6G5B_CSNK2B 0.661 0.935 0.004 0 HMGB2 0.849 CCR4 0.784 0.958 0.007 0 HMGB2 0.849 FAM118A 0.759 0.91 0.038 0.007 HMGB2 0.849 PYHIN1 0.807 0.937 0.033 0.003 HMGB2 0.849 LASS4 0.794 0.915 0.049 0.038 HMGB2 0.849 TSHZ2 0.801 0.957 0.004 0.002 HMGB2 0.849 CAMK1D 0.702 0.924 0.03 0 KLRD1 0.784 AMFR 0.69 0.841 0.013 0.038 KLRD1 0.784 HDHD1A 0.63 0.837 0.048 0.004 KLRD1 0.784 ZRANB1 0.724 0.872 0.004 0.016 KLRD1 0.784 UBE2F_C20orf194 0.762 0.882 0.002 0.017 KLRD1 0.784 HIST1H2BJ 0.697 0.858 0.035 0.005 KLRD1 0.784 CEP97 0.695 0.871 0.019 0.003 KLRD1 0.784 DYNLL1 0.776 0.897 0.016 0.002 KLRD1 0.784 FAM118B 0.737 0.868 0.024 0.024 KLRD1 0.784 SPARC 0.703 0.868 0.024 0.004 KLRD1 0.784 PLEKHA3 0.658 0.839 0.049 0.008 KLRD1 0.784 TMEM62_SPCS2_L 0.645 0.863 0.014 0.002 KLRD1 0.784 CMTM5 0.694 0.843 0.045 0.012 KLRD1 0.784 HIST2H2BF_HIST2 0.754 0.854 0.031 0.048 KLRD1 0.784 CALM2_C2orf61 0.733 0.86 0.008 0.018 CHI3L1 0.909 PRR13_PCBP2 0.77 0.962 0.045 0 CHI3L1 0.909 FFAR2 0.868 0.949 0.045 0.013 CHI3L1 0.909 SIAE 0.814 0.981 0.037 0 CHI3L1 0.909 ABCA13 0.868 0.98 0.028 0.004 CHI3L1 0.909 SAP30 0.728 0.944 0.016 0.001 CHI3L1 0.909 AGTRAP 0.856 0.957 0.023 0.02 CHI3L1 0.909 SUCNR1 0.922 0.996 0.02 0.006 CHI3L1 0.909 AIG1 0.842 0.969 0.046 0.003 CHI3L1 0.909 ACTA2 0.783 0.97 0.016 0.001 CHI3L1 0.909 TRIM21 0.874 0.984 0.018 0.01 CHI3L1 0.909 ICAM1 0.821 0.937 0.039 0.026 CHI3L1 0.909 ZRANB1 0.724 0.967 0.042 0 CHI3L1 0.909 UBE2F_C20orf194 0.762 0.968 0.033 0 CHI3L1 0.909 MYL9 0.771 0.97 0.045 0 CHI3L1 0.909 CYP4F3_CYP4F2 0.593 0.979 0.046 0 CHI3L1 0.909 FAM118B 0.737 0.98 0.047 0 CHI3L1 0.909 NP 0.633 0.965 0.047 0 CHI3L1 0.909 ATP6V0D1_LOC100 0.856 0.958 0.011 0.049 SLC39A9 0.551 DNAJC9_FAM149B1 0.811 0.882 0 0.001 SLC39A9 0.551 P4HA1_RPL17 0.715 0.792 0.001 0.028 SLC39A9 0.551 FAM118B 0.737 0.833 0 0.019 GIMAP7 0.755 NA 0.866 0.933 0.001 0.013 GIMAP7 0.755 CCR1 0.897 0.963 0 0.005 GIMAP7 0.755 MTHFS 0.757 0.87 0.005 0.021 GIMAP7 0.755 EIF2AK2 0.857 0.955 0 0.002 GIMAP7 0.755 SIAE 0.814 0.897 0.001 0.039 GIMAP7 0.755 MTRR 0.703 0.923 0 0 GIMAP7 0.755 AIG1 0.842 0.939 0 0.009 GIMAP7 0.755 GAB2 0.729 0.858 0.025 0.014 GIMAP7 0.755 HS2ST1_UBA2 0.758 0.865 0.009 0.026 GIMAP7 0.755 TREML1 0.793 0.863 0.045 0.033 GIMAP7 0.755 GSTO1 0.793 0.902 0.001 0.03 GIMAP7 0.755 CLEC4A 0.87 0.957 0 0.012 GIMAP7 0.755 GPR65 0.886 0.972 0 0.018 GIMAP7 0.755 MACF1 0.742 0.877 0.03 0.001 GIMAP7 0.755 HDHD1A 0.63 0.859 0.012 0 GIMAP7 0.755 LRRFIP1 0.673 0.887 0.025 0 GIMAP7 0.755 TRIM21 0.874 0.944 0 0.023 GIMAP7 0.755 TAF13 0.854 0.925 0 0.039 GIMAP7 0.755 P4HA1_RPL17 0.715 0.923 0.003 0 GIMAP7 0.755 KLHL5 0.826 0.954 0 0.002 GIMAP7 0.755 ANKRD28 0.8 0.924 0.001 0.004 GIMAP7 0.755 MGST3 0.602 0.84 0.03 0 GIMAP7 0.755 LAIR1_LAIR2 0.749 0.857 0.034 0.032 GIMAP7 0.755 ZRANB1 0.724 0.87 0.019 0.003 GIMAP7 0.755 UBE2F_C20orf194 0.762 0.852 0.044 0.012 GIMAP7 0.755 CCRL2 0.786 0.868 0.029 0.034 GIMAP7 0.755 ZNF587_ZNF417 0.648 0.865 0.049 0 GIMAP7 0.755 CEP97 0.695 0.891 0.013 0 GIMAP7 0.755 DYNLL1 0.776 0.915 0.004 0 GIMAP7 0.755 PLIN2 0.671 0.887 0.003 0.001 GIMAP7 0.755 FAM118B 0.737 0.966 0 0 GIMAP7 0.755 CTSL1_CTSLL3 0.715 0.862 0.024 0.005 GIMAP7 0.755 TMEM62_SPCS2_L 0.645 0.886 0.011 0 GIMAP7 0.755 PLEKHF2 0.822 0.914 0.002 0.014 GIMAP7 0.755 AIF1 0.816 0.953 0 0.002 GIMAP7 0.755 HIST2H2BF_HIST2 0.754 0.875 0.036 0.001 GIMAP7 0.755 CALM2_C2orf61 0.733 0.898 0.003 0.001 ANAPC11 0.818 NA 0.866 0.924 0.014 0.024 ANAPC11 0.818 FOLR3_FOLR2 0.848 0.93 0.008 0.007 ANAPC11 0.818 LGALS1 0.736 0.915 0.005 0.003 ANAPC11 0.818 MTHFS 0.757 0.911 0.004 0.003 ANAPC11 0.818 SIAE 0.814 0.917 0.004 0.016 ANAPC11 0.818 ABCA13 0.868 0.94 0.005 0.006 ANAPC11 0.818 CDA 0.804 0.912 0.006 0.016 ANAPC11 0.818 SAP30 0.728 0.86 0.046 0.025 ANAPC11 0.818 AIG1 0.842 0.937 0.006 0.006 ANAPC11 0.818 HS2ST1_UBA2 0.758 0.879 0.035 0.022 ANAPC11 0.818 VOPP1_LOC100128 0.618 0.88 0.034 0 ANAPC11 0.818 SLC11A1 0.785 0.903 0.008 0.031 ANAPC11 0.818 GSTO1 0.793 0.91 0.022 0.008 ANAPC11 0.818 CLEC4A 0.87 0.939 0.003 0.024 ANAPC11 0.818 NEK6_LOC1001290 0.765 0.895 0.005 0.017 ANAPC11 0.818 OR9A2 0.813 0.918 0.009 0.005 ANAPC11 0.818 DPH3 0.86 0.949 0.001 0.009 ANAPC11 0.818 TAF13 0.854 0.94 0.017 0.004 ANAPC11 0.818 KLHL5 0.826 0.903 0.028 0.034 ANAPC11 0.818 DLEU2_DLEU2L 0.874 0.95 0.001 0.014 ANAPC11 0.818 ANKRD28 0.8 0.928 0.005 0.006 ANAPC11 0.818 LHFP 0.759 0.866 0.048 0.02 ANAPC11 0.818 MYL9 0.771 0.895 0.036 0.009 ANAPC11 0.818 FAM118B 0.737 0.907 0.034 0.001 ANAPC11 0.818 AIF1 0.816 0.917 0.007 0.022 ANAPC11 0.818 CALM2_C2orf61 0.733 0.915 0.015 0 EXOSC4 0.731 DNAJC9_FAM149B1 0.811 0.864 0.043 0.032 EXOSC4 0.731 JUP 0.804 0.905 0.002 0.025 EXOSC4 0.731 C15orf54 0.821 0.878 0.013 0.017 EXOSC4 0.731 CFD 0.767 0.869 0.021 0.02 NA 0.866 RUNX2 0.848 0.943 0.001 0.047 NA 0.866 HLA-DRA 0.768 0.925 0.022 0.002 NA 0.866 NA 0.696 0.939 0.032 0 NA 0.866 PLA2G7 0.834 0.952 0.016 0.003 NA 0.866 PMS2CL_PMS2 0.685 0.92 0.049 0 NA 0.866 PDE3B 0.85 0.972 0.001 0.006 NA 0.866 SULF2 0.819 0.933 0.017 0.02 NA 0.866 ITGA4_CERKL 0.694 0.931 0.035 0 NA 0.866 NPCDR1 0.86 0.957 0.006 0.005 NA 0.866 NA 0.763 0.927 0.035 0.001 NA 0.866 LY6G5B_CSNK2B 0.661 0.934 0.017 0 NA 0.866 CCR4 0.784 0.949 0.009 0.001 NA 0.866 ABCG1 0.789 0.915 0.038 0.015 NA 0.866 FAM118A 0.759 0.921 0.026 0.002 NA 0.866 PYHIN1 0.807 0.949 0.024 0.001 NA 0.866 SYNE2 0.678 0.945 0.011 0 NA 0.866 TSHZ2 0.801 0.942 0.005 0.003 NA 0.866 RASA4_RASA4P_R 0.779 0.93 0.024 0.001 NA 0.866 CAMK1D 0.702 0.933 0.009 0 INSIG1 0.509 TAF13 0.854 0.904 0 0.029 INSIG1 0.509 CALM2_C2orf61 0.733 0.8 0 0.049 FOLR3_FOLR2 0.848 RUNX2 0.848 0.933 0.005 0.039 FOLR3_FOLR2 0.848 PHOSPHO1 0.85 0.944 0.003 0.024 FOLR3_FOLR2 0.848 PPIF 0.614 0.908 0.016 0 FOLR3_FOLR2 0.848 PDE3B 0.85 0.926 0.035 0.04 FOLR3_FOLR2 0.848 RRP12_LOC644215 0.632 0.905 0.038 0 FOLR3_FOLR2 0.848 LASS4 0.794 0.946 0 0.013 RUNX2 0.848 CCR1 0.897 0.94 0.019 0.025 RUNX2 0.848 FFAR2 0.868 0.935 0.014 0.014 RUNX2 0.848 MTHFS 0.757 0.943 0.022 0 RUNX2 0.848 SIAE 0.814 0.965 0.003 0 RUNX2 0.848 ABCA13 0.868 0.951 0.018 0.003 RUNX2 0.848 CDA 0.804 0.953 0.013 0 RUNX2 0.848 SAP30 0.728 0.95 0.008 0 RUNX2 0.848 AGTRAP 0.856 0.938 0.005 0.022 RUNX2 0.848 SUCNR1 0.922 0.97 0.003 0.009 RUNX2 0.848 AIG1 0.842 0.948 0.009 0.005 RUNX2 0.848 HS2ST1_UBA2 0.758 0.953 0.004 0 RUNX2 0.848 SLC11A1 0.785 0.959 0.013 0.001 RUNX2 0.848 CLEC4A 0.87 0.944 0.028 0.002 RUNX2 0.848 NEK6_LOC1001290 0.765 0.943 0.006 0.001 RUNX2 0.848 GPR65 0.886 0.933 0.027 0.018 RUNX2 0.848 DPH3 0.86 0.949 0.017 0.001 RUNX2 0.848 TRIM21 0.874 0.941 0.016 0.029 RUNX2 0.848 TAF13 0.854 0.943 0.029 0.002 RUNX2 0.848 KLHL5 0.826 0.941 0.024 0.001 RUNX2 0.848 DLEU2_DLEU2L 0.874 0.944 0.012 0.006 RUNX2 0.848 ANKRD28 0.8 0.979 0.002 0 RUNX2 0.848 MYL9 0.771 0.939 0.048 0 RUNX2 0.848 FAM118B 0.737 0.939 0.037 0 PRR13_PCBP2 0.77 PLA2G7 0.834 0.888 0.039 0.029 PRR13_PCBP2 0.77 C15orf54 0.821 0.879 0.041 0.04 PRR13_PCBP2 0.77 LY6G5B_CSNK2B 0.661 0.871 0.046 0 PRR13_PCBP2 0.77 CCR4 0.784 0.884 0.017 0.02 PRR13_PCBP2 0.77 RASA4_RASA4P_R 0.779 0.886 0.012 0.008 LGALS1 0.736 CCR4 0.784 0.865 0.036 0.005 LGALS1 0.736 CLC 0.81 0.884 0.03 0.026 LGALS1 0.736 LASS4 0.794 0.891 0.002 0.049 LGALS1 0.736 TSHZ2 0.801 0.872 0.022 0.027 CCR1 0.897 HLA-DRA 0.768 0.965 0.017 0 CCR1 0.897 NPCDR1 0.86 0.971 0.019 0.001 CCR1 0.897 CCR4 0.784 0.961 0.012 0 HLA-DRA 0.768 FFAR2 0.868 0.949 0.001 0.002 HLA-DRA 0.768 EIF2AK2 0.857 0.942 0.003 0.006 HLA-DRA 0.768 AIG1 0.842 0.91 0.008 0.032 HLA-DRA 0.768 GSTO1 0.793 0.893 0.016 0.018 HLA-DRA 0.768 CLEC4A 0.87 0.967 0 0.011 HLA-DRA 0.768 GPR65 0.886 0.959 0 0.034 HLA-DRA 0.768 ACTA2 0.783 0.878 0.043 0.017 HLA-DRA 0.768 TRIM21 0.874 0.985 0 0.002 HLA-DRA 0.768 ICAM1 0.821 0.933 0.002 0.005 HLA-DRA 0.768 P4HA1_RPL17 0.715 0.883 0.032 0 HLA-DRA 0.768 KLHL5 0.826 0.952 0 0.002 HLA-DRA 0.768 HSPB1_HSPBL2 0.778 0.867 0.021 0.048 HLA-DRA 0.768 IL1B 0.871 0.945 0.001 0.023 HLA-DRA 0.768 CCRL2 0.786 0.892 0.029 0.007 HLA-DRA 0.768 PLIN2 0.671 0.874 0.045 0 HLA-DRA 0.768 CTSL1_CTSLL3 0.715 0.887 0.018 0.001 HLA-DRA 0.768 TMEM62_SPCS2_L 0.645 0.874 0.017 0 HLA-DRA 0.768 PLEKHF2 0.822 0.916 0.002 0.007 HLA-DRA 0.768 AIF1 0.816 0.952 0 0.004 CD163 0.735 NA 0.696 0.834 0.014 0.044 CD163 0.735 PLA2G7 0.834 0.918 0.004 0.018 CD163 0.735 PMS2CL_PMS2 0.685 0.818 0.03 0.034 CD163 0.735 SULF2 0.819 0.933 0 0.034 CD163 0.735 NA 0.718 0.857 0.017 0.022 CD163 0.735 LY6G5B_CSNK2B 0.661 0.835 0.007 0.023 CD163 0.735 NA 0.746 0.881 0.002 0.007 CD163 0.735 CAMK1D 0.702 0.866 0.004 0.013 CD163 0.735 CFD 0.767 0.874 0.01 0.031 FFAR2 0.868 NPCDR1 0.86 0.958 0.013 0.001 FFAR2 0.868 CCR4 0.784 0.934 0.034 0 FFAR2 0.868 ABCG1 0.789 0.937 0.017 0.006 PHOSPHO1 0.85 SIAE 0.814 0.949 0.003 0.006 PHOSPHO1 0.85 ABCA13 0.868 0.981 0.003 0.001 PHOSPHO1 0.85 CDA 0.804 0.919 0.021 0.023 PHOSPHO1 0.85 SAP30 0.728 0.933 0.004 0.001 PHOSPHO1 0.85 SUCNR1 0.922 0.99 0.001 0.009 PHOSPHO1 0.85 NEK6_LOC1001290 0.765 0.904 0.017 0.02 PHOSPHO1 0.85 AMFR 0.69 0.93 0.015 0 PHOSPHO1 0.85 DPH3 0.86 0.951 0.004 0.01 PHOSPHO1 0.85 ICAM1 0.821 0.919 0.011 0.033 PHOSPHO1 0.85 TAF13 0.854 0.931 0.019 0.01 PHOSPHO1 0.85 DLEU2_DLEU2L 0.874 0.947 0.007 0.019 PHOSPHO1 0.85 ANKRD28 0.8 0.932 0.044 0.005 PHOSPHO1 0.85 HSPB1_HSPBL2 0.778 0.933 0.013 0.003 PHOSPHO1 0.85 LHFP 0.759 0.92 0.036 0.001 PHOSPHO1 0.85 ZRANB1 0.724 0.94 0.029 0 PHOSPHO1 0.85 PTGS1 0.68 0.965 0.007 0 PHOSPHO1 0.85 UBE2F_C20orf194 0.762 0.949 0.015 0 PHOSPHO1 0.85 MYL9 0.771 0.98 0.005 0 PHOSPHO1 0.85 CMTM5 0.694 0.969 0.012 0 PHOSPHO1 0.85 AIF1 0.816 0.922 0.03 0.008 PPIF 0.614 C22orf37 0.582 0.774 0.027 0.004 PPIF 0.614 OR9A2 0.813 0.881 0 0.02 PPIF 0.614 ICAM1 0.821 0.898 0 0.042 PPIF 0.614 IL1B 0.871 0.935 0 0.035 PPIF 0.614 MYL9 0.771 0.83 0.003 0.04 PPIF 0.614 CALM2_C2orf61 0.733 0.801 0.011 0.039 MTHFS 0.757 DNAJC9_FAM149B1 0.811 0.904 0.024 0.002 MTHFS 0.757 PLA2G7 0.834 0.891 0.014 0.03 MTHFS 0.757 PMS2CL_PMS2 0.685 0.863 0.035 0.001 MTHFS 0.757 PDE3B 0.85 0.985 0 0.002 MTHFS 0.757 C15orf54 0.821 0.884 0.019 0.014 MTHFS 0.757 CCR4 0.784 0.911 0.003 0.001 MTHFS 0.757 FAM118A 0.759 0.863 0.021 0.023 MTHFS 0.757 PYHIN1 0.807 0.887 0.016 0.011 MTHFS 0.757 CLC 0.81 0.89 0.025 0.018 MTHFS 0.757 SYNE2 0.678 0.861 0.042 0 MTHFS 0.757 TSHZ2 0.801 0.899 0.003 0.013 MTHFS 0.757 RASA4_RASA4P_R 0.779 0.859 0.014 0.036 MTHFS 0.757 CAMK1D 0.702 0.889 0.008 0 MTHFS 0.757 CFD 0.767 0.878 0.025 0.008 MTHFS 0.757 IL5RA 0.832 0.883 0.02 0.024 DNAJC9_FAM149B1 0.811 ABCA13 0.868 0.932 0.005 0.049 DNAJC9_FAM149B1 0.811 MTRR 0.703 0.916 0 0.004 DNAJC9_FAM149B1 0.811 AIG1 0.842 0.953 0 0.021 DNAJC9_FAM149B1 0.811 HS2ST1_UBA2 0.758 0.887 0.005 0.05 DNAJC9_FAM149B1 0.811 GSTO1 0.793 0.912 0.004 0.048 DNAJC9_FAM149B1 0.811 MACF1 0.742 0.927 0.002 0.003 DNAJC9_FAM149B1 0.811 AMFR 0.69 0.853 0.022 0.045 DNAJC9_FAM149B1 0.811 GALNT2 0.651 0.86 0.039 0.01 DNAJC9_FAM149B1 0.811 OR9A2 0.813 0.897 0.018 0.037 DNAJC9_FAM149B1 0.811 HDHD1A 0.63 0.884 0.011 0.002 DNAJC9_FAM149B1 0.811 LRRFIP1 0.673 0.919 0.007 0 DNAJC9_FAM149B1 0.811 P4HA1_RPL17 0.715 0.933 0.001 0.001 DNAJC9_FAM149B1 0.811 MGST3 0.602 0.877 0.02 0 DNAJC9_FAM149B1 0.811 CCDC125 0.632 0.886 0.019 0 DNAJC9_FAM149B1 0.811 PDK4 0.71 0.922 0.008 0.002 DNAJC9_FAM149B1 0.811 LAIR1_LAIR2 0.749 0.887 0.007 0.05 DNAJC9_FAM149B1 0.811 FSD1L_GARNL1 0.66 0.889 0.004 0.003 DNAJC9_FAM149B1 0.811 UBE2F_C20orf194 0.762 0.88 0.026 0.037 DNAJC9_FAM149B1 0.811 CEP97 0.695 0.906 0.006 0.002 DNAJC9_FAM149B1 0.811 DYNLL1 0.776 0.959 0.001 0 DNAJC9_FAM149B1 0.811 PLIN2 0.671 0.872 0.022 0.007 DNAJC9_FAM149B1 0.811 FAM118B 0.737 0.927 0.001 0.006 DNAJC9_FAM149B1 0.811 CTSL1_CTSLL3 0.715 0.897 0.039 0.002 DNAJC9_FAM149B1 0.811 NP 0.633 0.876 0.013 0.001 DNAJC9_FAM149B1 0.811 TMEM62_SPCS2_L 0.645 0.895 0.004 0.001 DNAJC9_FAM149B1 0.811 CALM2_C2orf61 0.733 0.907 0.002 0.007 LCN2 0.752 HIST1H3J 0.722 0.874 0.016 0.002 LCN2 0.752 HIST1H3A 0.715 0.876 0.005 0.005 LCN2 0.752 RRP12_LOC644215 0.632 0.794 0.03 0.047 LCN2 0.752 B4GALT3 0.659 0.855 0.001 0.014 LCN2 0.752 C4orf3 0.629 0.818 0.023 0.012 LCN2 0.752 RPIA 0.713 0.843 0.039 0.027 LCN2 0.752 C1orf128 0.751 0.864 0.013 0.049 EIF2AK2 0.857 PLA2G7 0.834 0.957 0.011 0.002 EIF2AK2 0.857 PMS2CL_PMS2 0.685 0.922 0.037 0 EIF2AK2 0.857 PDE3B 0.85 0.956 0.008 0.008 EIF2AK2 0.857 SULF2 0.819 0.944 0.022 0.002 EIF2AK2 0.857 HSP90AB1_HSP90A 0.799 0.964 0.007 0.001 EIF2AK2 0.857 ITGA4_CERKL 0.694 0.953 0.002 0 EIF2AK2 0.857 NPCDR1 0.86 0.959 0.002 0.006 EIF2AK2 0.857 NA 0.763 0.954 0.008 0 EIF2AK2 0.857 CCR4 0.784 0.961 0.002 0 EIF2AK2 0.857 FAM118A 0.759 0.907 0.036 0.005 EIF2AK2 0.857 PYHIN1 0.807 0.939 0.015 0.004 EIF2AK2 0.857 SYNE2 0.678 0.932 0.015 0 EIF2AK2 0.857 NA 0.746 0.95 0.007 0 EIF2AK2 0.857 TSHZ2 0.801 0.951 0.003 0.002 EIF2AK2 0.857 RASA4_RASA4P_R 0.779 0.925 0.021 0.001 EIF2AK2 0.857 MPZL2 0.612 0.914 0.044 0 EIF2AK2 0.857 IL5RA 0.832 0.943 0.035 0.002 SIAE 0.814 PDE3B 0.85 0.95 0.011 0.002 SIAE 0.814 SULF2 0.819 0.926 0.005 0.011 SIAE 0.814 NPCDR1 0.86 0.916 0.011 0.033 SIAE 0.814 CCR4 0.784 0.919 0.019 0 SIAE 0.814 FAM118A 0.759 0.918 0.013 0.001 SIAE 0.814 PYHIN1 0.807 0.911 0.036 0.004 SIAE 0.814 LASS4 0.794 0.945 0.001 0.007 SIAE 0.814 TSHZ2 0.801 0.931 0.004 0.002 SIAE 0.814 RASA4_RASA4P_R 0.779 0.906 0.029 0.001 ABCA13 0.868 PDE3B 0.85 0.947 0.005 0.016 ABCA13 0.868 NPCDR1 0.86 0.924 0.035 0.033 ABCA13 0.868 CCR4 0.784 0.925 0.03 0.002 ABCA13 0.868 CLC 0.81 0.939 0.02 0.009 ABCA13 0.868 LASS4 0.794 0.953 0.001 0.009 ABCA13 0.868 B4GALT3 0.659 0.945 0.017 0 ABCA13 0.868 IL5RA 0.832 0.925 0.042 0.014 NA 0.696 MTRR 0.703 0.826 0.032 0.006 NA 0.696 MACF1 0.742 0.866 0.004 0.003 NA 0.696 GALNT2 0.651 0.805 0.03 0.012 NA 0.696 P4HA1_RPL17 0.715 0.825 0.024 0.008 NA 0.696 ZRANB1 0.724 0.81 0.023 0.043 NA 0.696 CCRL2 0.786 0.892 0 0.012 NA 0.696 IRF1 0.651 0.776 0.049 0.05 NA 0.696 DYNLL1 0.776 0.839 0.008 0.028 NA 0.696 FAM118B 0.737 0.826 0.015 0.045 EFCAB2 0.96 PMS2CL_PMS2 0.685 0.988 0.047 0 EFCAB2 0.96 PDE3B 0.85 0.988 0.022 0.001 EFCAB2 0.96 CFD 0.767 0.988 0.05 0 HINT1 0.658 DYNLL1 0.776 0.894 0 0.004 HINT1 0.658 CALM2_C2orf61 0.733 0.856 0 0.041 HIST1H3J 0.722 C22orf37 0.582 0.758 0.037 0.032 HIST1H3J 0.722 AREG 0.598 0.76 0.033 0.046 HIST1H3J 0.722 DAAM2_LOC100131 0.664 0.799 0.021 0.016 HIST1H3J 0.722 CEACAM8 0.748 0.844 0.015 0.029 HIST1H3J 0.722 MINPP1 0.497 0.816 0.018 0 HIST1H3J 0.722 HIST1H2BM 0.504 0.794 0.04 0.001 HIST1H3J 0.722 ITGA2B 0.699 0.83 0.002 0.039 HIST1H3J 0.722 NA 0.705 0.82 0.007 0.045 HIST1H3J 0.722 HIST1H2BJ 0.697 0.865 0 0.015 HIST1H3J 0.722 CMTM5 0.694 0.863 0 0.021 HIST1H3J 0.722 IFI44 0.762 0.842 0.014 0.046 HIST1H3J 0.722 KIAA0101_CSNK1G 0.581 0.858 0.015 0 CDA 0.804 PDE3B 0.85 0.929 0.009 0.023 CDA 0.804 JUP 0.804 0.896 0.014 0.04 CDA 0.804 NPCDR1 0.86 0.914 0.009 0.049 CDA 0.804 LY6G5B_CSNK2B 0.661 0.892 0.038 0 CDA 0.804 CCR4 0.784 0.925 0.006 0.001 CDA 0.804 PYHIN1 0.807 0.901 0.03 0.01 CDA 0.804 CLC 0.81 0.92 0.023 0.008 CDA 0.804 LASS4 0.794 0.902 0.018 0.042 CDA 0.804 TSHZ2 0.801 0.894 0.023 0.016 CDA 0.804 RASA4_RASA4P_R 0.779 0.895 0.026 0.004 CDA 0.804 IL5RA 0.832 0.903 0.027 0.016 SAP30 0.728 PLA2G7 0.834 0.859 0.043 0.031 SAP30 0.728 PDE3B 0.85 0.942 0 0.002 SAP30 0.728 SULF2 0.819 0.864 0.034 0.033 SAP30 0.728 C15orf54 0.821 0.857 0.038 0.03 SAP30 0.728 IL5RA 0.832 0.862 0.049 0.02 AGTRAP 0.856 LASS4 0.794 0.944 0.028 0.001 SUCNR1 0.922 PDE3B 0.85 0.976 0.038 0.001 SUCNR1 0.922 B4GALT3 0.659 0.977 0.018 0 MTRR 0.703 PLA2G7 0.834 0.897 0.004 0.007 MTRR 0.703 ITGA4_CERKL 0.694 0.839 0.012 0.012 MTRR 0.703 NPCDR1 0.86 0.92 0 0.039 MTRR 0.703 NA 0.763 0.868 0.005 0.002 MTRR 0.703 LY6G5B_CSNK2B 0.661 0.839 0.006 0.006 MTRR 0.703 CCR4 0.784 0.89 0 0.016 MTRR 0.703 PYHIN1 0.807 0.904 0.001 0.027 MTRR 0.703 CLC 0.81 0.881 0.003 0.034 MTRR 0.703 NA 0.746 0.832 0.029 0.02 MTRR 0.703 TSHZ2 0.801 0.904 0 0.039 MTRR 0.703 CAMK1D 0.702 0.821 0.025 0.018 PLA2G7 0.834 CLEC4A 0.87 0.956 0.001 0.036 PLA2G7 0.834 TRAF3 0.594 0.888 0.013 0 PLA2G7 0.834 OR9A2 0.813 0.928 0.005 0.017 PLA2G7 0.834 ACTA2 0.783 0.901 0.023 0.031 PLA2G7 0.834 TAF13 0.854 0.947 0.002 0.028 PLA2G7 0.834 P4HA1_RPL17 0.715 0.923 0.005 0 PLA2G7 0.834 KLHL5 0.826 0.932 0.001 0.033 PLA2G7 0.834 LHFP 0.759 0.863 0.043 0.039 PLA2G7 0.834 CCRL2 0.786 0.907 0.038 0.014 PLA2G7 0.834 DYNLL1 0.776 0.92 0.023 0.001 PLA2G7 0.834 PLIN2 0.671 0.89 0.01 0.004 PLA2G7 0.834 FAM118B 0.737 0.929 0.005 0.002 PLA2G7 0.834 CTSL1_CTSLL3 0.715 0.901 0.035 0.002 PLA2G7 0.834 TMEM62_SPCS2_L 0.645 0.894 0.039 0 PLA2G7 0.834 PLEKHF2 0.822 0.929 0.003 0.026 PLA2G7 0.834 AIF1 0.816 0.939 0.002 0.022 PLA2G7 0.834 PPP1R2_PPP1R2P3 0.569 0.893 0.048 0 PLA2G7 0.834 HIST2H2BF_HIST2 0.754 0.905 0.036 0.004 PLA2G7 0.834 CALM2_C2orf61 0.733 0.918 0.006 0.001 AIG1 0.842 PMS2CL_PMS2 0.685 0.937 0.031 0 AIG1 0.842 PDE3B 0.85 0.955 0.014 0.006 AIG1 0.842 IRF4 0.781 0.923 0.017 0.005 AIG1 0.842 HSP90AB1_HSP90A 0.799 0.94 0.023 0.003 AIG1 0.842 ITGA4_CERKL 0.694 0.92 0.04 0 AIG1 0.842 NPCDR1 0.86 0.941 0.009 0.012 AIG1 0.842 LY6G5B_CSNK2B 0.661 0.913 0.034 0 AIG1 0.842 CCR4 0.784 0.946 0.007 0 AIG1 0.842 CLC 0.81 0.929 0.038 0.01 AIG1 0.842 TSHZ2 0.801 0.924 0.024 0.005 AIG1 0.842 RASA4_RASA4P_R 0.779 0.921 0.019 0.002 AIG1 0.842 IL5RA 0.832 0.921 0.039 0.008 PCOLCE2 0.709 NA 0.763 0.817 0.046 0.017 PCOLCE2 0.709 CFD 0.767 0.84 0.019 0.044 GAB2 0.729 SULF2 0.819 0.932 0 0.011 GAB2 0.729 HSP90AB1_HSP90A 0.799 0.878 0.015 0.03 GAB2 0.729 NA 0.718 0.872 0.033 0 GAB2 0.729 LY6G5B_CSNK2B 0.661 0.866 0.026 0 GAB2 0.729 CCR4 0.784 0.883 0.006 0.018 GAB2 0.729 CLC 0.81 0.859 0.03 0.047 GAB2 0.729 NA 0.746 0.872 0.03 0 GAB2 0.729 RASA4_RASA4P_R 0.779 0.887 0.003 0.003 GAB2 0.729 CAMK1D 0.702 0.876 0.014 0.002 HS2ST1_UBA2 0.758 PDE3B 0.85 0.963 0 0.008 HS2ST1_UBA2 0.758 NPCDR1 0.86 0.908 0.003 0.041 HS2ST1_UBA2 0.758 C15orf54 0.821 0.882 0.037 0.006 HS2ST1_UBA2 0.758 CCR4 0.784 0.892 0.013 0.002 HS2ST1_UBA2 0.758 ABCG1 0.789 0.89 0.011 0.049 HS2ST1_UBA2 0.758 TSHZ2 0.801 0.907 0.005 0.007 HS2ST1_UBA2 0.758 RASA4_RASA4P_R 0.779 0.869 0.024 0.012 HS2ST1_UBA2 0.758 IL5RA 0.832 0.885 0.021 0.017 HIST1H3A 0.715 C22orf37 0.582 0.77 0.015 0.024 HIST1H3A 0.715 DAAM2_LOC100131 0.664 0.801 0.032 0.018 HIST1H3A 0.715 CEACAM8 0.748 0.841 0.029 0.019 HIST1H3A 0.715 HIST1H2BM 0.504 0.79 0.046 0.001 HIST1H3A 0.715 ITGA2B 0.699 0.82 0.012 0.05 HIST1H3A 0.715 HIST1H2BJ 0.697 0.854 0.001 0.027 HIST1H3A 0.715 CMTM5 0.694 0.856 0 0.03 HIST1H3A 0.715 SDHC 0.627 0.821 0.041 0.003 C22orf37 0.582 PMS2CL_PMS2 0.685 0.775 0.011 0.027 HLA-DPA1 0.65 TRAF3 0.594 0.767 0.036 0.015 HLA-DPA1 0.65 P4HA1_RPL17 0.715 0.823 0.006 0.015 HLA-DPA1 0.65 PDK4 0.71 0.808 0.027 0.016 HLA-DPA1 0.65 CCRL2 0.786 0.888 0 0.031 HLA-DPA1 0.65 IRF1 0.651 0.779 0.021 0.043 HLA-DPA1 0.65 FAM118B 0.737 0.832 0.005 0.028 HLA-DPA1 0.65 TMEM62_SPCS2_L 0.645 0.772 0.026 0.032 VOPP1_LOC100128 0.618 PMS2CL_PMS2 0.685 0.766 0.037 0.05 SLC11A1 0.785 PDE3B 0.85 0.937 0.005 0.016 SLC11A1 0.785 CCR4 0.784 0.896 0.028 0.003 SLC11A1 0.785 RRP12_LOC644215 0.632 0.867 0.036 0.001 SLC11A1 0.785 ABCG1 0.789 0.913 0.014 0.023 SLC11A1 0.785 LASS4 0.794 0.915 0.005 0.033 ABCA1 0.866 CCR4 0.784 0.916 0.029 0.015 ABCA1 0.866 ABCG1 0.789 0.966 0.005 0.007 ABCA1 0.866 TSHZ2 0.801 0.921 0.034 0.032 ABCA1 0.866 CAMK1D 0.702 0.914 0.035 0.001 DAAM2_LOC100131 0.664 IRS2 0.648 0.825 0 0.05 LTF 0.732 B4GALT3 0.659 0.814 0.014 0.037 TREML1 0.793 IRS2 0.648 0.861 0.036 0.011 TREML1 0.793 C15orf54 0.821 0.929 0.006 0.004 TREML1 0.793 CCR4 0.784 0.901 0.004 0.025 TREML1 0.793 CLC 0.81 0.897 0.046 0.028 TREML1 0.793 B4GALT3 0.659 0.871 0.028 0.005 TREML1 0.793 CFD 0.767 0.886 0.046 0.022 TREML1 0.793 IL5RA 0.832 0.907 0.017 0.048 GSTO1 0.793 HSP90AB1_HSP90A 0.799 0.896 0.028 0.021 GSTO1 0.793 NPCDR1 0.86 0.93 0.002 0.029 GSTO1 0.793 NA 0.763 0.911 0.044 0 GSTO1 0.793 C15orf54 0.821 0.904 0.048 0.001 GSTO1 0.793 CCR4 0.784 0.933 0.006 0 GSTO1 0.793 ABCG1 0.789 0.893 0.011 0.033 GSTO1 0.793 PYHIN1 0.807 0.908 0.035 0.003 GSTO1 0.793 CLC 0.81 0.937 0.013 0.003 GSTO1 0.793 TSHZ2 0.801 0.923 0.005 0.006 GSTO1 0.793 RASA4_RASA4P_R 0.779 0.89 0.013 0.016 GSTO1 0.793 IL5RA 0.832 0.923 0.026 0.005 CEACAM8 0.748 B4GALT3 0.659 0.84 0 0.03 CLEC4A 0.87 PDE3B 0.85 0.952 0.015 0.018 CLEC4A 0.87 ITGA4_CERKL 0.694 0.952 0.031 0 CLEC4A 0.87 NPCDR1 0.86 0.938 0.048 0.006 CLEC4A 0.87 LY6G5B_CSNK2B 0.661 0.933 0.024 0 CLEC4A 0.87 CCR4 0.784 0.952 0.017 0 CLEC4A 0.87 ABCG1 0.789 0.955 0.007 0.001 CLEC4A 0.87 TSHZ2 0.801 0.954 0.009 0.001 CLEC4A 0.87 CAMK1D 0.702 0.938 0.028 0 PMS2CL_PMS2 0.685 MACF1 0.742 0.852 0.007 0.007 PMS2CL_PMS2 0.685 LRRFIP1 0.673 0.821 0.024 0.003 PMS2CL_PMS2 0.685 DPH3 0.86 0.928 0 0.033 PMS2CL_PMS2 0.685 TAF13 0.854 0.913 0 0.036 PMS2CL_PMS2 0.685 P4HA1_RPL17 0.715 0.848 0.011 0.002 PMS2CL_PMS2 0.685 ZRANB1 0.724 0.823 0.009 0.035 PMS2CL_PMS2 0.685 NA 0.705 0.833 0.032 0.002 PMS2CL_PMS2 0.685 UBE2F_C20orf194 0.762 0.899 0 0.006 PMS2CL_PMS2 0.685 CEP97 0.695 0.832 0.011 0.016 PMS2CL_PMS2 0.685 DYNLL1 0.776 0.87 0.003 0.006 PMS2CL_PMS2 0.685 FAM118B 0.737 0.841 0.005 0.043 PMS2CL_PMS2 0.685 PLEKHA3 0.658 0.861 0.001 0.001 PMS2CL_PMS2 0.685 TMEM62_SPCS2_L 0.645 0.79 0.04 0.023 PMS2CL_PMS2 0.685 PLEKHF2 0.822 0.912 0 0.012 PMS2CL_PMS2 0.685 AIF1 0.816 0.916 0 0.02 PMS2CL_PMS2 0.685 CALM2_C2orf61 0.733 0.897 0 0.001 PDE3B 0.85 NEK6_LOC1001290 0.765 0.906 0.026 0.016 PDE3B 0.85 OR9A2 0.813 0.943 0.042 0 PDE3B 0.85 DPH3 0.86 0.962 0.012 0.001 PDE3B 0.85 TRIM21 0.874 0.962 0.001 0.036 PDE3B 0.85 ICAM1 0.821 0.915 0.014 0.023 PDE3B 0.85 TAF13 0.854 0.959 0.017 0.004 PDE3B 0.85 KLHL5 0.826 0.944 0.019 0.01 PDE3B 0.85 DLEU2_DLEU2L 0.874 0.986 0.001 0.001 PDE3B 0.85 ANKRD28 0.8 0.995 0.001 0 PDE3B 0.85 UBE2F_C20orf194 0.762 0.929 0.045 0 PDE3B 0.85 CEP97 0.695 0.929 0.04 0 PDE3B 0.85 FAM118B 0.737 0.941 0.016 0.001 PDE3B 0.85 PLEKHF2 0.822 0.961 0.021 0 PDE3B 0.85 AIF1 0.816 0.934 0.042 0.008 PDE3B 0.85 CALM2_C2orf61 0.733 0.978 0.002 0 SULF2 0.819 ACTA2 0.783 0.926 0.013 0.005 SULF2 0.819 ICAM1 0.821 0.929 0.002 0.038 SULF2 0.819 ANKRD28 0.8 0.904 0.024 0.036 SULF2 0.819 HSPB1_HSPBL2 0.778 0.895 0.038 0.017 SULF2 0.819 ZRANB1 0.724 0.908 0.031 0.001 SULF2 0.819 PLIN2 0.671 0.906 0.008 0.001 SULF2 0.819 FAM118B 0.737 0.928 0.008 0.001 SULF2 0.819 CALM2_C2orf61 0.733 0.89 0.043 0.003 NEK6_LOC1001290 0.765 CCR4 0.784 0.872 0.045 0.008 NEK6_LOC1001290 0.765 ABCG1 0.789 0.89 0.037 0.024 NEK6_LOC1001290 0.765 LASS4 0.794 0.887 0.028 0.014 CENPK 0.688 NA 0.705 0.811 0.011 0.039 CENPK 0.688 DYNLL1 0.776 0.864 0.001 0.05 TRAF3 0.594 ITGA4_CERKL 0.694 0.807 0.002 0.039 TRAF3 0.594 NPCDR1 0.86 0.901 0 0.048 TRAF3 0.594 NA 0.718 0.788 0.016 0.04 TRAF3 0.594 SYNE2 0.678 0.775 0.01 0.034 TRAF3 0.594 NA 0.746 0.827 0.003 0.004 TRAF3 0.594 CAMK1D 0.702 0.82 0.001 0.013 GPR65 0.886 ITGA4_CERKL 0.694 0.969 0.047 0 MACF1 0.742 HSP90AB1_HSP90A 0.799 0.913 0 0.016 MACF1 0.742 ITGA4_CERKL 0.694 0.85 0.012 0.008 MACF1 0.742 NPCDR1 0.86 0.925 0 0.025 MACF1 0.742 NA 0.718 0.834 0.025 0.031 MACF1 0.742 NA 0.763 0.86 0.006 0.019 MACF1 0.742 LY6G5B_CSNK2B 0.661 0.874 0.001 0.002 MACF1 0.742 CCR4 0.784 0.904 0 0.015 MACF1 0.742 RRP12_LOC644215 0.632 0.803 0.04 0.007 MACF1 0.742 PYHIN1 0.807 0.902 0.001 0.042 MACF1 0.742 SON 0.604 0.806 0.043 0.005 MACF1 0.742 SYNE2 0.678 0.881 0.001 0.001 MACF1 0.742 NA 0.746 0.884 0.002 0.003 MACF1 0.742 TSHZ2 0.801 0.918 0 0.024 MACF1 0.742 CAMK1D 0.702 0.881 0.001 0.003 MACF1 0.742 LOC100128751 0.651 0.822 0.021 0.016 AMFR 0.69 CCR4 0.784 0.873 0.008 0.008 AMFR 0.69 B4GALT3 0.659 0.869 0.012 0 RPL17_SNORD58B 0.694 HDHD1A 0.63 0.786 0.011 0.031 RPL17_SNORD58B 0.694 MGST3 0.602 0.772 0.02 0.033 RPL17_SNORD58B 0.694 FSD1L_GARNL1 0.66 0.812 0.006 0.028 RPL17_SNORD58B 0.694 NA 0.705 0.842 0.006 0.008 RPL17_SNORD58B 0.694 UBE2F_C20orf194 0.762 0.866 0 0.048 RPL17_SNORD58B 0.694 CEP97 0.695 0.838 0.001 0.025 RPL17_SNORD58B 0.694 DYNLL1 0.776 0.925 0 0 RPL17_SNORD58B 0.694 PLEKHA3 0.658 0.834 0 0.021 RPL17_SNORD58B 0.694 TMEM62_SPCS2_L 0.645 0.797 0.003 0.048 RPL17_SNORD58B 0.694 CALM2_C2orf61 0.733 0.909 0 0.001 IRS2 0.648 ITGA2B 0.699 0.831 0.038 0.001 IRS2 0.648 LHFP 0.759 0.879 0 0.032 IRS2 0.648 PTGS1 0.68 0.817 0.04 0.008 IRS2 0.648 MYL9 0.771 0.879 0.002 0.024 IRS2 0.648 CMTM5 0.694 0.814 0.047 0.005 GALNT2 0.651 HSP90AB1_HSP90A 0.799 0.861 0.005 0.024 GALNT2 0.651 NPCDR1 0.86 0.904 0 0.047 GALNT2 0.651 PYHIN1 0.807 0.852 0.005 0.045 GALNT2 0.651 NA 0.746 0.805 0.029 0.011 GALNT2 0.651 RASA4_RASA4P_R 0.779 0.85 0.001 0.049 HSP90AB1_HSP90A 0.799 ACTA2 0.783 0.931 0.001 0.012 HSP90AB1_HSP90A 0.799 TRIM21 0.874 0.977 0 0.014 HSP90AB1_HSP90A 0.799 P4HA1_RPL17 0.715 0.898 0.035 0 HSP90AB1_HSP90A 0.799 HSPB1_HSPBL2 0.778 0.903 0.005 0.029 HSP90AB1_HSP90A 0.799 LAIR1_LAIR2 0.749 0.883 0.037 0.008 HSP90AB1_HSP90A 0.799 UBE2F_C20orf194 0.762 0.91 0.033 0.001 HSP90AB1_HSP90A 0.799 CCRL2 0.786 0.904 0.044 0.008 HSP90AB1_HSP90A 0.799 DYNLL1 0.776 0.922 0.025 0 HSP90AB1_HSP90A 0.799 PLIN2 0.671 0.895 0.027 0.001 HSP90AB1_HSP90A 0.799 FAM118B 0.737 0.91 0.017 0.001 HSP90AB1_HSP90A 0.799 NP 0.633 0.911 0.031 0 HSP90AB1_HSP90A 0.799 TMEM62_SPCS2_L 0.645 0.886 0.01 0 OR9A2 0.813 C15orf54 0.821 0.895 0.034 0.033 OR9A2 0.813 CCR4 0.784 0.908 0.004 0.007 OR9A2 0.813 FAM118A 0.759 0.871 0.031 0.028 OR9A2 0.813 TSHZ2 0.801 0.914 0.003 0.016 OR9A2 0.813 RASA4_RASA4P_R 0.779 0.885 0.015 0.018 OR9A2 0.813 CAMK1D 0.702 0.899 0.011 0.001 OR9A2 0.813 CFD 0.767 0.924 0.016 0.001 OR9A2 0.813 IL5RA 0.832 0.914 0.018 0.03 HDHD1A 0.63 ITGA4_CERKL 0.694 0.799 0.01 0.029 HDHD1A 0.63 NA 0.763 0.825 0.004 0.047 HDHD1A 0.63 CCR4 0.784 0.879 0 0.018 HDHD1A 0.63 S100B 0.72 0.795 0.033 0.02 HDHD1A 0.63 SYNE2 0.678 0.797 0.013 0.015 ACTA2 0.783 C15orf54 0.821 0.889 0.029 0.022 ACTA2 0.783 CCR4 0.784 0.882 0.011 0.027 ACTA2 0.783 PYHIN1 0.807 0.892 0.032 0.03 ACTA2 0.783 RASA4_RASA4P_R 0.779 0.892 0.023 0.004 ACTA2 0.783 IL5RA 0.832 0.896 0.021 0.021 ACPL2 0.651 TAS2R31 0.685 0.778 0.039 0.05 LRRFIP1 0.673 SFRS9 0.527 0.794 0.004 0.002 LRRFIP1 0.673 NPCDR1 0.86 0.916 0 0.047 LRRFIP1 0.673 LY6G5B_CSNK2B 0.661 0.877 0 0.004 LRRFIP1 0.673 CCR4 0.784 0.906 0 0.012 LRRFIP1 0.673 S100B 0.72 0.804 0.05 0.022 LRRFIP1 0.673 SON 0.604 0.754 0.031 0.04 LRRFIP1 0.673 SYNE2 0.678 0.846 0.001 0.005 LRRFIP1 0.673 NA 0.746 0.861 0.001 0.008 LRRFIP1 0.673 TSHZ2 0.801 0.93 0 0.01 LRRFIP1 0.673 CAMK1D 0.702 0.903 0 0.002 OCR1 0.647 NF-E4 0.628 0.823 0.015 0.001 OCR1 0.647 PDK4 0.71 0.819 0.011 0.041 OCR1 0.647 CYP4F3_CYP4F2 0.593 0.754 0.044 0.046 OCR1 0.647 DYNLL1 0.776 0.859 0 0.041 OCR1 0.647 SPARC 0.703 0.839 0.001 0.026 OCR1 0.647 FBXL13 0.614 0.807 0.03 0.002 ITGA4_CERKL 0.694 TRIM21 0.874 0.953 0 0.014 ITGA4_CERKL 0.694 TAF13 0.854 0.922 0 0.035 ITGA4_CERKL 0.694 P4HA1_RPL17 0.715 0.889 0.003 0 ITGA4_CERKL 0.694 KLHL5 0.826 0.923 0 0.043 ITGA4_CERKL 0.694 DLEU2_DLEU2L 0.874 0.943 0 0.036 ITGA4_CERKL 0.694 MGST3 0.602 0.798 0.027 0.005 ITGA4_CERKL 0.694 CCRL2 0.786 0.867 0.003 0.027 ITGA4_CERKL 0.694 ZNF587_ZNF417 0.648 0.816 0.036 0.004 ITGA4_CERKL 0.694 CEP97 0.695 0.832 0.029 0.005 ITGA4_CERKL 0.694 DYNLL1 0.776 0.884 0.002 0.002 ITGA4_CERKL 0.694 PLIN2 0.671 0.821 0.026 0.013 ITGA4_CERKL 0.694 FAM118B 0.737 0.895 0.003 0.001 ITGA4_CERKL 0.694 CTSL1_CTSLL3 0.715 0.845 0.009 0.014 ITGA4_CERKL 0.694 PLEKHA3 0.658 0.822 0.022 0.005 ITGA4_CERKL 0.694 TMEM62_SPCS2_L 0.645 0.887 0 0 ITGA4_CERKL 0.694 PLEKHF2 0.822 0.935 0 0.004 ITGA4_CERKL 0.694 HIST2H2BF_HIST2 0.754 0.842 0.013 0.033 ITGA4_CERKL 0.694 CALM2_C2orf61 0.733 0.863 0.002 0.007 DPH3 0.86 NA 0.763 0.938 0.038 0 DPH3 0.86 CCR4 0.784 0.944 0.019 0 DPH3 0.86 CLC 0.81 0.944 0.029 0.003 DPH3 0.86 LASS4 0.794 0.917 0.021 0.02 DPH3 0.86 TSHZ2 0.801 0.92 0.037 0.005 DPH3 0.86 C4orf3 0.629 0.927 0.048 0 DPH3 0.86 CAMK1D 0.702 0.917 0.043 0 DPH3 0.86 IL5RA 0.832 0.94 0.05 0.002 ERGIC1 0.628 LY6G5B_CSNK2B 0.661 0.784 0.024 0.019 ERGIC1 0.628 CCR4 0.784 0.859 0 0.039 ERGIC1 0.628 CAMK1D 0.702 0.838 0.002 0.012 CD300A 0.623 CAMK1D 0.702 0.792 0.016 0.045 NF-E4 0.628 KIAA1324 0.666 0.778 0.015 0.045 TRIM21 0.874 NPCDR1 0.86 0.956 0.032 0.001 TRIM21 0.874 LY6G5B_CSNK2B 0.661 0.952 0.037 0 TRIM21 0.874 CCR4 0.784 0.957 0.019 0 NPCDR1 0.86 ICAM1 0.821 0.937 0.003 0.017 NPCDR1 0.86 TAF13 0.854 0.935 0.021 0.014 NPCDR1 0.86 KLHL5 0.826 0.943 0.004 0.008 NPCDR1 0.86 ANKRD28 0.8 0.917 0.033 0.006 NPCDR1 0.86 HSPB1_HSPBL2 0.778 0.91 0.029 0.009 NPCDR1 0.86 IL1B 0.871 0.951 0.004 0.017 NPCDR1 0.86 CCRL2 0.786 0.935 0.016 0.001 NPCDR1 0.86 PLEKHF2 0.822 0.919 0.046 0.012 NPCDR1 0.86 AIF1 0.816 0.932 0.008 0.01 NA 0.718 P4HA1_RPL17 0.715 0.849 0.005 0.007 NA 0.763 P4HA1_RPL17 0.715 0.879 0.004 0.002 NA 0.763 DLEU2_DLEU2L 0.874 0.942 0 0.035 NA 0.763 ZRANB1 0.724 0.834 0.05 0.029 NA 0.763 UBE2F_C20orf194 0.762 0.878 0.001 0.02 NA 0.763 CEP97 0.695 0.874 0.005 0.003 NA 0.763 DYNLL1 0.776 0.898 0.01 0.001 NA 0.763 PLIN2 0.671 0.845 0.009 0.009 NA 0.763 FAM118B 0.737 0.873 0.003 0.014 NA 0.763 CTSL1_CTSLL3 0.715 0.862 0.002 0.018 NA 0.763 HIST2H2BF_HIST2 0.754 0.854 0.03 0.023 NA 0.763 CALM2_C2orf61 0.733 0.869 0.004 0.011 ICAM1 0.821 CCR4 0.784 0.9 0.045 0.004 ICAM1 0.821 ABCG1 0.789 0.916 0.012 0.018 ICAM1 0.821 RASA4_RASA4P_R 0.779 0.914 0.045 0.001 TAF13 0.854 CCR4 0.784 0.935 0.009 0.002 TAF13 0.854 ABCG1 0.789 0.922 0.019 0.005 TAF13 0.854 PYHIN1 0.807 0.929 0.038 0.009 TAF13 0.854 CLC 0.81 0.946 0.027 0.003 TAF13 0.854 LASS4 0.794 0.913 0.011 0.017 TAF13 0.854 TSHZ2 0.801 0.924 0.01 0.016 TAF13 0.854 RASA4_RASA4P_R 0.779 0.91 0.034 0.007 P4HA1_RPL17 0.715 LY6G5B_CSNK2B 0.661 0.857 0.001 0.004 P4HA1_RPL17 0.715 CCR4 0.784 0.893 0 0.032 P4HA1_RPL17 0.715 PYHIN1 0.807 0.917 0 0.015 P4HA1_RPL17 0.715 CLC 0.81 0.905 0 0.022 P4HA1_RPL17 0.715 SON 0.604 0.847 0.002 0.001 P4HA1_RPL17 0.715 SYNE2 0.678 0.875 0 0.002 P4HA1_RPL17 0.715 NA 0.746 0.87 0.002 0.005 P4HA1_RPL17 0.715 TSHZ2 0.801 0.911 0 0.035 P4HA1_RPL17 0.715 CAMK1D 0.702 0.884 0 0.002 P4HA1_RPL17 0.715 LOC100128751 0.651 0.817 0.02 0.01 P4HA1_RPL17 0.715 IL5RA 0.832 0.894 0.001 0.047 P4HA1_RPL17 0.715 TAS2R31 0.685 0.833 0.035 0.001 P4HA1_RPL17 0.715 C1orf128 0.751 0.813 0.028 0.022 C15orf54 0.821 ANKRD28 0.8 0.929 0 0.013 C15orf54 0.821 ITGA2B 0.699 0.881 0.05 0.002 C15orf54 0.821 LHFP 0.759 0.894 0.001 0.024 C15orf54 0.821 ZRANB1 0.724 0.898 0.022 0.001 C15orf54 0.821 PTGS1 0.68 0.886 0.014 0.002 C15orf54 0.821 UBE2F_C20orf194 0.762 0.912 0.003 0.004 C15orf54 0.821 MYL9 0.771 0.915 0.003 0.008 C15orf54 0.821 HIST1H2BJ 0.697 0.91 0.018 0.001 C15orf54 0.821 CTSL1_CTSLL3 0.715 0.875 0.049 0.004 C15orf54 0.821 SPARC 0.703 0.905 0.01 0.001 C15orf54 0.821 PLEKHA3 0.658 0.897 0.012 0 C15orf54 0.821 CMTM5 0.694 0.888 0.031 0.001 C15orf54 0.821 HIST2H2BF_HIST2 0.754 0.884 0.03 0.013 C15orf54 0.821 CALM2_C2orf61 0.733 0.905 0.008 0.002 KLHL5 0.826 LY6G5B_CSNK2B 0.661 0.931 0.002 0 KLHL5 0.826 CCR4 0.784 0.958 0.002 0 KLHL5 0.826 ABCG1 0.789 0.932 0.002 0.003 KLHL5 0.826 TSHZ2 0.801 0.928 0.01 0.004 KLHL5 0.826 RASA4_RASA4P_R 0.779 0.905 0.02 0.008 KLHL5 0.826 CAMK1D 0.702 0.902 0.044 0 KLHL5 0.826 IL5RA 0.832 0.919 0.048 0.004 DLEU2_DLEU2L 0.874 CCR4 0.784 0.947 0.009 0 DLEU2_DLEU2L 0.874 ABCG1 0.789 0.939 0.042 0.001 DLEU2_DLEU2L 0.874 IL5RA 0.832 0.936 0.049 0.005 ANKRD28 0.8 LY6G5B_CSNK2B 0.661 0.904 0.006 0 ANKRD28 0.8 CCR4 0.784 0.946 0.005 0 ANKRD28 0.8 ABCG1 0.789 0.897 0.024 0.015 ANKRD28 0.8 FAM118A 0.759 0.906 0.006 0.005 ANKRD28 0.8 PYHIN1 0.807 0.926 0.01 0.003 ANKRD28 0.8 SYNE2 0.678 0.891 0.045 0 ANKRD28 0.8 TSHZ2 0.801 0.933 0.001 0.007 ANKRD28 0.8 RASA4_RASA4P_R 0.779 0.9 0.023 0.004 ANKRD28 0.8 CAMK1D 0.702 0.907 0.017 0 LY6G5B_CSNK2B 0.661 LAIR1_LAIR2 0.749 0.886 0 0.008 LY6G5B_CSNK2B 0.661 ZRANB1 0.724 0.811 0.01 0.032 LY6G5B_CSNK2B 0.661 UBE2F_C20orf194 0.762 0.84 0.003 0.029 LY6G5B_CSNK2B 0.661 FSD1L 0.7 0.783 0.043 0.035 LY6G5B_CSNK2B 0.661 ZNF587_ZNF417 0.648 0.833 0.013 0 LY6G5B_CSNK2B 0.661 KDM6B_TMEM88 0.711 0.839 0 0.047 LY6G5B_CSNK2B 0.661 CEP97 0.695 0.85 0.005 0.001 LY6G5B_CSNK2B 0.661 IRF1 0.651 0.774 0.024 0.044 LY6G5B_CSNK2B 0.661 DYNLL1 0.776 0.856 0.005 0.006 LY6G5B_CSNK2B 0.661 PLIN2 0.671 0.801 0.023 0.018 LY6G5B_CSNK2B 0.661 FAM118B 0.737 0.885 0.001 0.001 LY6G5B_CSNK2B 0.661 PLEKHA3 0.658 0.773 0.04 0.021 LY6G5B_CSNK2B 0.661 AIF1 0.816 0.902 0 0.019 LY6G5B_CSNK2B 0.661 HIST2H2BF_HIST2 0.754 0.826 0.007 0.05 LY6G5B_CSNK2B 0.661 CALM2_C2orf61 0.733 0.826 0.008 0.017 MGST3 0.602 CCR4 0.784 0.868 0 0.024 HSPB1_HSPBL2 0.778 CCR4 0.784 0.874 0.047 0.009 HSPB1_HSPBL2 0.778 PYHIN1 0.807 0.884 0.033 0.021 HSPB1_HSPBL2 0.778 RASA4_RASA4P_R 0.779 0.883 0.023 0.005 HSPB1_HSPBL2 0.778 IL5RA 0.832 0.89 0.048 0.019 CCR4 0.784 LHFP 0.759 0.85 0.013 0.032 CCR4 0.784 LAIR1_LAIR2 0.749 0.885 0.03 0.008 CCR4 0.784 ZRANB1 0.724 0.878 0.022 0.001 CCR4 0.784 UBE2F_C20orf194 0.762 0.923 0.002 0.001 CCR4 0.784 ZNF587_ZNF417 0.648 0.882 0.05 0 CCR4 0.784 MYL9 0.771 0.903 0.006 0.003 CCR4 0.784 CEP97 0.695 0.93 0.003 0 CCR4 0.784 DYNLL1 0.776 0.91 0.013 0 CCR4 0.784 PLIN2 0.671 0.872 0.021 0.001 CCR4 0.784 FAM118B 0.737 0.94 0.001 0 CCR4 0.784 NP 0.633 0.874 0.018 0 CCR4 0.784 PLEKHA3 0.658 0.869 0.046 0 CCR4 0.784 TMEM62_SPCS2_L 0.645 0.872 0.038 0 CCR4 0.784 PLEKHF2 0.822 0.923 0.005 0.002 CCR4 0.784 AIF1 0.816 0.946 0 0.006 CCR4 0.784 HIST2H2BF_HIST2 0.754 0.887 0.049 0.001 CCR4 0.784 CALM2_C2orf61 0.733 0.923 0.002 0 CCDC125 0.632 SYNE2 0.678 0.804 0.002 0.036 ABCG1 0.789 PLEKHF2 0.822 0.908 0.012 0.008 ABCG1 0.789 AIF1 0.816 0.905 0.003 0.05 ITGA2B 0.699 B4GALT3 0.659 0.801 0.019 0.05 LHFP 0.759 LASS4 0.794 0.91 0.001 0.016 LHFP 0.759 TSHZ2 0.801 0.861 0.021 0.049 LAIR1_LAIR2 0.749 PYHIN1 0.807 0.888 0.024 0.028 LAIR1_LAIR2 0.749 TSHZ2 0.801 0.896 0.002 0.045 ZRANB1 0.724 FAM118A 0.759 0.867 0.002 0.033 ZRANB1 0.724 PYHIN1 0.807 0.884 0.003 0.031 ZRANB1 0.724 CLC 0.81 0.88 0.006 0.021 ZRANB1 0.724 NA 0.746 0.855 0.012 0.004 ZRANB1 0.724 TSHZ2 0.801 0.899 0 0.047 ZRANB1 0.724 CAMK1D 0.702 0.855 0.004 0.006 ZRANB1 0.724 IL5RA 0.832 0.886 0.003 0.047 ZRANB1 0.724 C1orf128 0.751 0.866 0.015 0.006 TIMM10 0.554 IFI44 0.762 0.82 0.004 0.024 FSD1L_GARNL1 0.66 TAS2R31 0.685 0.811 0.026 0.002 HIST1H2AJ_HIST1 0.631 POLE2 0.539 0.689 0.007 0.007 PTGS1 0.68 CLC 0.81 0.852 0.011 0.05 PTGS1 0.68 B4GALT3 0.659 0.825 0.008 0.01 PTGS1 0.68 CFD 0.767 0.843 0.012 0.047 PTGS1 0.68 C1orf128 0.751 0.849 0.01 0.047 UBE2F_C20orf194 0.762 FAM118A 0.759 0.87 0.012 0.018 UBE2F_C20orf194 0.762 PYHIN1 0.807 0.895 0.005 0.008 UBE2F_C20orf194 0.762 CLC 0.81 0.906 0.007 0.019 UBE2F_C20orf194 0.762 B4GALT3 0.659 0.922 0 0 UBE2F_C20orf194 0.762 RASA4_RASA4P_R 0.779 0.878 0.001 0.038 UBE2F_C20orf194 0.762 C4orf3 0.629 0.851 0.025 0 UBE2F_C20orf194 0.762 RPIA 0.713 0.884 0.035 0 UBE2F_C20orf194 0.762 IL5RA 0.832 0.906 0.003 0.025 UBE2F_C20orf194 0.762 C1orf128 0.751 0.92 0.007 0 FAM118A 0.759 DYNLL1 0.776 0.878 0.043 0.001 FAM118A 0.759 FAM118B 0.737 0.88 0.027 0.002 FAM118A 0.759 CALM2_C2orf61 0.733 0.862 0.036 0.004 CCRL2 0.786 CLC 0.81 0.908 0.016 0.029 CCRL2 0.786 NA 0.746 0.887 0.014 0.003 CCRL2 0.786 TSHZ2 0.801 0.905 0.003 0.024 CCRL2 0.786 IL5RA 0.832 0.912 0.007 0.029 SRXN1 0.612 RPIA 0.713 0.799 0.017 0.025 SRXN1 0.612 C1orf128 0.751 0.848 0.002 0.022 ZNF587_ZNF417 0.648 SYNE2 0.678 0.849 0.001 0.008 ZNF587_ZNF417 0.648 CAMK1D 0.702 0.857 0 0.018 PYHIN1 0.807 MYL9 0.771 0.876 0.046 0.03 PYHIN1 0.807 CEP97 0.695 0.91 0.018 0 PYHIN1 0.807 DYNLL1 0.776 0.91 0.047 0 PYHIN1 0.807 PLIN2 0.671 0.879 0.04 0.002 PYHIN1 0.807 FAM118B 0.737 0.927 0.004 0.001 PYHIN1 0.807 TMEM62_SPCS2_L 0.645 0.904 0.009 0 PYHIN1 0.807 DCTN5 0.557 0.879 0.046 0 KIAA1324 0.666 CYP4F3_CYP4F2 0.593 0.765 0.039 0.033 KDM6B_TMEM88 0.711 RASA4_RASA4P_R 0.779 0.852 0.012 0.022 KDM6B_TMEM88 0.711 CAMK1D 0.702 0.835 0.041 0.004 MYL9 0.771 CLC 0.81 0.886 0.048 0.013 MYL9 0.771 LASS4 0.794 0.926 0 0.048 MYL9 0.771 B4GALT3 0.659 0.881 0.022 0 MYL9 0.771 CFD 0.767 0.882 0.04 0.006 MYL9 0.771 IL5RA 0.832 0.903 0.012 0.025 MYL9 0.771 C1orf128 0.751 0.879 0.037 0.014 TAAR1 0.657 TAS2R31 0.685 0.763 0.016 0.049 CLC 0.81 CEP97 0.695 0.908 0.016 0.001 CLC 0.81 DYNLL1 0.776 0.914 0.033 0.001 CLC 0.81 PLIN2 0.671 0.893 0.02 0.002 CLC 0.81 FAM118B 0.737 0.914 0.008 0.002 CLC 0.81 CTSL1_CTSLL3 0.715 0.903 0.034 0.001 CLC 0.81 PLEKHF2 0.822 0.923 0.014 0.021 CLC 0.81 CALM2_C2orf61 0.733 0.908 0.018 0.002 CEP97 0.695 SYNE2 0.678 0.845 0.011 0.002 CEP97 0.695 B4GALT3 0.659 0.814 0.01 0.019 CEP97 0.695 CAMK1D 0.702 0.825 0.014 0.032 CEP97 0.695 IL5RA 0.832 0.902 0 0.025 SON 0.604 DYNLL1 0.776 0.837 0.002 0.027 SON 0.604 FAM118B 0.737 0.835 0.001 0.027 SON 0.604 TMEM62_SPCS2_L 0.645 0.768 0.02 0.019 SON 0.604 CALM2_C2orf61 0.733 0.816 0.002 0.036 IRF1 0.651 NA 0.746 0.818 0.015 0.01 IRF1 0.651 CAMK1D 0.702 0.797 0.024 0.027 IRF1 0.651 IL5RA 0.832 0.884 0 0.041 IRF1 0.651 C1orf128 0.751 0.803 0.042 0.038 SYNE2 0.678 DYNLL1 0.776 0.882 0.001 0.005 SYNE2 0.678 FAM118B 0.737 0.887 0 0.004 SYNE2 0.678 NP 0.633 0.778 0.027 0.035 SYNE2 0.678 TMEM62_SPCS2_L 0.645 0.814 0.007 0.011 SYNE2 0.678 CALM2_C2orf61 0.733 0.843 0.001 0.025 SYNE2 0.678 DCTN5 0.557 0.78 0.026 0.003 DYNLL1 0.776 NA 0.746 0.862 0.014 0.012 DYNLL1 0.776 B4GALT3 0.659 0.857 0.007 0.006 DYNLL1 0.776 CAMK1D 0.702 0.883 0.001 0.004 DYNLL1 0.776 IL5RA 0.832 0.91 0.002 0.038 DYNLL1 0.776 C1orf128 0.751 0.885 0.013 0.006 NA 0.746 PLIN2 0.671 0.821 0.024 0.025 NA 0.746 FAM118B 0.737 0.85 0.009 0.023 NA 0.746 HIST2H2BF_HIST2 0.754 0.875 0.002 0.018 TSHZ2 0.801 PLIN2 0.671 0.919 0.009 0 TSHZ2 0.801 FAM118B 0.737 0.935 0.002 0 TSHZ2 0.801 PLEKHF2 0.822 0.902 0.04 0.009 TSHZ2 0.801 AIF1 0.816 0.92 0.007 0.013 PLIN2 0.671 CAMK1D 0.702 0.805 0.02 0.034 FAM118B 0.737 RASA4_RASA4P_R 0.779 0.874 0.002 0.042 FAM118B 0.737 CAMK1D 0.702 0.881 0.002 0.002 FAM118B 0.737 CFD 0.767 0.855 0.045 0.01 FAM118B 0.737 IL5RA 0.832 0.895 0.003 0.038 B4GALT3 0.659 NP 0.633 0.84 0.007 0.001 B4GALT3 0.659 PLEKHA3 0.658 0.761 0.036 0.049 B4GALT3 0.659 TMEM62_SPCS2_L 0.645 0.752 0.044 0.044 B4GALT3 0.659 CMTM5 0.694 0.816 0.019 0.015 B4GALT3 0.659 CALM2_C2orf61 0.733 0.84 0.001 0.024 RASA4_RASA4P_R 0.779 PLEKHF2 0.822 0.877 0.037 0.045 RASA4_RASA4P_R 0.779 AIF1 0.816 0.898 0.01 0.013 NP 0.633 C1orf128 0.751 0.849 0.003 0.014 PLEKHF2 0.822 IL5RA 0.832 0.915 0.027 0.017 CAMK1D 0.702 AIF1 0.816 0.906 0 0.049 CAMK1D 0.702 HIST2H2BF_HIST2 0.754 0.839 0.011 0.049 CAMK1D 0.702 CALM2_C2orf61 0.733 0.856 0.006 0.006 CAMK1D 0.702 SPATA6 0.642 0.832 0.035 0.001 AIF1 0.816 IL5RA 0.832 0.926 0.034 0.005 CFD 0.767 HIST2H2BF_HIST2 0.754 0.861 0.036 0.025 CFD 0.767 CALM2_C2orf61 0.733 0.879 0.01 0.007 MPZL2 0.612 IFI44 0.762 0.809 0.016 0.009 IL5RA 0.832 HIST2H2BF_HIST2 0.754 0.905 0.021 0.008 IL5RA 0.832 CALM2_C2orf61 0.733 0.901 0.033 0.002 CALM2_C2orf61 0.733 C1orf128 0.751 0.855 0.025 0.007

TABLE 18 Ratios inSIRS Versus ipSIRS Gene 1 Name Gene 1 AUC Gene 2 Name Gene 2 AUC Ratio AUC Ratio Signif to Gene 1 Ratio Signif to Gene 2 TLR5 0.725 CLEC4D 0.732 0.868 0.016 0.015 TLR5 0.725 SLC37A3 0.668 0.907 0.001 0 TLR5 0.725 BMX_HNRPDL 0.716 0.877 0.027 0.001 TLR5 0.725 FKBP5_LOC285847 0.79 0.901 0.003 0.026 TLR5 0.725 MMP9_LOC1001280 0.729 0.861 0.01 0.02 TLR5 0.725 PFKFB2 0.777 0.888 0.011 0.007 TLR5 0.725 ODZ1 0.777 0.891 0.015 0.002 TLR5 0.725 SH3PXD2B 0.751 0.873 0.032 0.009 TLR5 0.725 PRR13_PCBP2 0.758 0.898 0 0.048 TLR5 0.725 CD163 0.808 0.887 0.022 0.019 TLR5 0.725 MTHFS 0.733 0.909 0 0.013 TLR5 0.725 PLA2G7 0.738 0.868 0.019 0.005 TLR5 0.725 KDM6B_TMEM88 0.672 0.842 0 0.049 TLR5 0.725 HIST2H2BF_HIST2 0.62 0.82 0.035 0.007 CD177 0.611 ARG1 0.715 0.816 0.01 0.014 VNN1 0.523 ARG1 0.715 0.842 0 0.012 UBE2J1 0.814 SLC37A3 0.668 0.943 0.011 0 UBE2J1 0.814 CLEC4E 0.849 0.918 0.031 0.022 UBE2J1 0.814 PPP2R5A_SNORA16 0.716 0.935 0.015 0 UBE2J1 0.814 PRR13_PCBP2 0.758 0.918 0.029 0.003 UBE2J1 0.814 MTHFS 0.733 0.96 0.005 0 UBE2J1 0.814 CDA 0.694 0.902 0.043 0.001 UBE2J1 0.814 HAL 0.846 0.916 0.021 0.045 UBE2J1 0.814 CCDC125 0.79 0.906 0.043 0.011 UBE2J1 0.814 RGS2 0.789 0.935 0.006 0.005 IMP3 0.908 RUNX2 0.868 0.98 0.018 0.016 IMP3 0.908 SYNE2 0.886 0.99 0.049 0.003 RNASE2_LOC64333 0.718 CLEC4D 0.732 0.836 0.045 0.016 RNASE2_LOC64333 0.718 SLC37A3 0.668 0.858 0.007 0.004 RNASE2_LOC64333 0.718 MMP9_LOC1001280 0.729 0.857 0.006 0.037 RNASE2_LOC64333 0.718 PFKFB2 0.777 0.855 0.031 0.026 RNASE2_LOC64333 0.718 CD163 0.808 0.881 0.017 0.004 RNASE2_LOC64333 0.718 MTHFS 0.733 0.862 0.006 0.027 RNASE2_LOC64333 0.718 PLA2G7 0.738 0.874 0.022 0.003 CLEC4D 0.732 C3AR1 0.777 0.875 0.008 0.038 CLEC4D 0.732 FAR2 0.706 0.842 0.035 0.019 CLEC4D 0.732 OMG 0.769 0.901 0.004 0.009 CLEC4D 0.732 TDRD9 0.752 0.904 0.014 0 CLEC4D 0.732 ACER3 0.842 0.926 0 0.048 CLEC4D 0.732 GPR84 0.71 0.874 0.043 0 CLEC4D 0.732 PLB1 0.722 0.904 0.005 0.001 CLEC4D 0.732 DSE 0.784 0.907 0.001 0.026 CLEC4D 0.732 GSR 0.605 0.822 0.019 0.008 CLEC4D 0.732 SMPDL3A 0.736 0.911 0.003 0.001 CLEC4D 0.732 ATP13A3 0.796 0.894 0.007 0.042 CLEC4D 0.732 PDGFC 0.822 0.943 0.001 0.009 CLEC4D 0.732 SEC24A_SAR1B 0.739 0.86 0.004 0.046 CLEC4D 0.732 EXOSC4 0.742 0.957 0 0.001 CLEC4D 0.732 CCR1 0.72 0.848 0.017 0.039 CLEC4D 0.732 AP3B2 0.697 0.843 0.046 0.015 CLEC4D 0.732 PCOLCE2 0.667 0.889 0.003 0.002 CLEC4D 0.732 GALNT2 0.673 0.892 0.001 0.002 CLEC4D 0.732 HDHD1A 0.7 0.823 0.026 0.039 CLEC4D 0.732 KIAA0746 0.713 0.854 0.038 0.007 CLEC4D 0.732 PLEKHF2 0.737 0.868 0.005 0.039 CLEC4D 0.732 IGLV6-57 0.757 0.849 0.025 0.034 C3AR1 0.777 SLC37A3 0.668 0.863 0.037 0.001 C3AR1 0.777 ERLIN1 0.756 0.868 0.048 0.033 C3AR1 0.777 FKBP5_LOC285847 0.79 0.898 0.009 0.046 C3AR1 0.777 MMP9_LOC1001280 0.729 0.884 0.011 0.02 C3AR1 0.777 PFKFB2 0.777 0.887 0.035 0.026 C3AR1 0.777 CDA 0.694 0.86 0.022 0.02 C3AR1 0.777 SLC11A1 0.705 0.842 0.044 0.04 C3AR1 0.777 ACPL2 0.756 0.868 0.035 0.024 C3AR1 0.777 CPM 0.812 0.89 0.036 0.046 ARG1 0.715 TDRD9 0.752 0.906 0 0.008 ARG1 0.715 GPR84 0.71 0.828 0.018 0.044 ARG1 0.715 HPGD 0.655 0.864 0.008 0.003 ARG1 0.715 HSPC159 0.577 0.792 0.04 0.012 ARG1 0.715 FOLR3_FOLR2 0.585 0.795 0.026 0.02 ARG1 0.715 LCN2 0.639 0.851 0.008 0.003 ARG1 0.715 ABCA13 0.699 0.868 0.001 0.026 ARG1 0.715 MKI67 0.674 0.835 0.009 0.043 ARG1 0.715 LTF 0.642 0.809 0.036 0.022 ARG1 0.715 RETN 0.473 0.792 0.033 0 ARG1 0.715 ANKRD28 0.71 0.858 0.002 0.04 ARG1 0.715 LHFP 0.561 0.783 0.026 0.017 ARG1 0.715 TAAR1 0.589 0.81 0.029 0.005 ARG1 0.715 DEFA4_DEFA8P 0.663 0.829 0.022 0.013 ARG1 0.715 SPARC 0.661 0.802 0.044 0.033 ARG1 0.715 IGJ 0.683 0.833 0.028 0.011 FCGR1A_FCGR1B_(—) 0.797 IFI16 0.721 0.902 0.006 0.008 FCGR1A_FCGR1B_(—) 0.797 PRR13_PCBP2 0.758 0.895 0.005 0.025 FCGR1A_FCGR1B_(—) 0.797 MTHFS 0.733 0.914 0.003 0.006 FCGR1A_FCGR1B_(—) 0.797 KDM6B_TMEM88 0.672 0.869 0.022 0.007 C11orf82 0.927 MMP9_LOC1001280 0.729 0.976 0.025 0 C11orf82 0.927 PPP2R5A_SNORA16 0.716 0.974 0.049 0 C11orf82 0.927 PRR13_PCBP2 0.758 0.974 0.016 0 C11orf82 0.927 HIST1H2AA 0.825 0.973 0.033 0.002 C11orf82 0.927 CDA 0.694 0.965 0.022 0 C11orf82 0.927 SYNE2 0.886 0.971 0.043 0.023 FAR2 0.706 BMX_HNRPDL 0.716 0.85 0.037 0.003 FAR2 0.706 CLEC4E 0.849 0.954 0 0.013 FAR2 0.706 ERLIN1 0.756 0.897 0.006 0.001 FAR2 0.706 FKBP5_LOC285847 0.79 0.907 0.001 0.022 FAR2 0.706 PFKFB2 0.777 0.904 0.004 0.003 FAR2 0.706 ECHDC3 0.823 0.941 0.001 0.004 FAR2 0.706 PPP2R5A_SNORA16 0.716 0.869 0.002 0.023 FAR2 0.706 HMGB2 0.673 0.868 0.009 0.003 FAR2 0.706 TPST1 0.754 0.874 0.024 0.001 FAR2 0.706 CD163 0.808 0.873 0.036 0.023 FAR2 0.706 SAP30 0.789 0.913 0 0.044 FAR2 0.706 DAAM2_LOC100131 0.778 0.89 0.024 0.001 FAR2 0.706 ACPL2 0.756 0.892 0.004 0.006 FAR2 0.706 CCDC125 0.79 0.913 0 0.049 FAR2 0.706 THBS1 0.698 0.851 0.039 0.001 FAR2 0.706 CPM 0.812 0.908 0.003 0.022 FAR2 0.706 CAMK1D 0.735 0.882 0.002 0.016 GALNT3 0.859 CLEC4E 0.849 0.944 0.026 0.018 GALNT3 0.859 IFI16 0.721 0.923 0.041 0.002 GALNT3 0.859 ECHDC3 0.823 0.929 0.043 0.01 GALNT3 0.859 PPP2R5A_SNORA16 0.716 0.962 0.003 0 GALNT3 0.859 PRR13_PCBP2 0.758 0.93 0.02 0.007 GALNT3 0.859 MTHFS 0.733 0.946 0.022 0.001 GALNT3 0.859 SAP30 0.789 0.917 0.019 0.022 GALNT3 0.859 AREG 0.814 0.916 0.028 0.032 GALNT3 0.859 ERGIC1 0.758 0.92 0.022 0.003 GALNT3 0.859 NA 0.884 0.959 0.012 0.028 GALNT3 0.859 HAL 0.846 0.933 0.011 0.037 GALNT3 0.859 KIAA1257_ACAD9/ 0.823 0.926 0.022 0.02 GALNT3 0.859 CCDC125 0.79 0.938 0.016 0.01 GALNT3 0.859 RGS2 0.789 0.945 0.002 0.018 OMG 0.769 SLC37A3 0.668 0.902 0.01 0 OMG 0.769 CLEC4E 0.849 0.947 0 0.036 OMG 0.769 FKBP5_LOC285847 0.79 0.915 0.007 0.013 OMG 0.769 MMP9_LOC1001280 0.729 0.888 0.014 0.004 OMG 0.769 PFKFB2 0.777 0.906 0.016 0.003 OMG 0.769 IFI16 0.721 0.845 0.033 0.047 OMG 0.769 ODZ1 0.777 0.906 0.036 0.001 OMG 0.769 ECHDC3 0.823 0.934 0.001 0.024 OMG 0.769 PPP2R5A_SNORA16 0.716 0.916 0.001 0.005 OMG 0.769 HMGB2 0.673 0.917 0.011 0 OMG 0.769 PRR13_PCBP2 0.758 0.89 0.002 0.041 OMG 0.769 TPST1 0.754 0.926 0.019 0 OMG 0.769 MTHFS 0.733 0.893 0.012 0.012 OMG 0.769 CDA 0.694 0.873 0.011 0.012 OMG 0.769 SLC11A1 0.705 0.898 0.003 0.008 OMG 0.769 DAAM2_LOC100131 0.778 0.899 0.035 0.004 OMG 0.769 ACPL2 0.756 0.935 0.003 0.002 OMG 0.769 ERGIC1 0.758 0.903 0.003 0.03 OMG 0.769 HAL 0.846 0.947 0 0.039 OMG 0.769 CCDC125 0.79 0.913 0.001 0.039 OMG 0.769 ZRANB1 0.718 0.85 0.028 0.038 OMG 0.769 CYP4F3_CYP4F2 0.755 0.868 0.035 0.029 OMG 0.769 MME 0.847 0.94 0.001 0.027 OMG 0.769 CPM 0.812 0.908 0.009 0.031 OMG 0.769 RBP7 0.803 0.939 0.001 0.014 OMG 0.769 RGS2 0.789 0.94 0 0.023 OMG 0.769 CAMK1D 0.735 0.864 0.007 0.048 SLC37A3 0.668 TDRD9 0.752 0.894 0.007 0 SLC37A3 0.668 ACER3 0.842 0.934 0 0.02 SLC37A3 0.668 C7orf53 0.711 0.847 0.006 0.012 SLC37A3 0.668 PLB1 0.722 0.922 0 0 SLC37A3 0.668 DSE 0.784 0.924 0 0.005 SLC37A3 0.668 GSR 0.605 0.773 0.042 0.026 SLC37A3 0.668 SMPDL3A 0.736 0.861 0.004 0.019 SLC37A3 0.668 ATP13A3 0.796 0.919 0.001 0.004 SLC37A3 0.668 PDGFC 0.822 0.941 0 0.014 SLC37A3 0.668 EXOSC4 0.742 0.924 0 0.003 SLC37A3 0.668 CCR1 0.72 0.826 0.015 0.027 SLC37A3 0.668 ABCA13 0.699 0.8 0.046 0.03 SLC37A3 0.668 PCOLCE2 0.667 0.832 0.001 0.026 SLC37A3 0.668 MKI67 0.674 0.79 0.028 0.026 SLC37A3 0.668 MACF1 0.699 0.817 0.025 0.019 SLC37A3 0.668 GALNT2 0.673 0.866 0 0.008 SLC37A3 0.668 HDHD1A 0.7 0.829 0.004 0.024 SLC37A3 0.668 KIAA0746 0.713 0.847 0.007 0.008 SLC37A3 0.668 LAIR1_LAIR2 0.777 0.874 0.001 0.03 SLC37A3 0.668 CD151 0.767 0.892 0 0.018 SLC37A3 0.668 IGK@_IGKC_IGKV 0.774 0.848 0.017 0.04 SLC37A3 0.668 MYL9 0.695 0.816 0.019 0.028 SLC37A3 0.668 HIST1H2BJ 0.797 0.874 0.001 0.047 SLC37A3 0.668 IGLV6-57 0.757 0.851 0.007 0.025 SLC37A3 0.668 SDHC 0.85 0.907 0.001 0.042 BMX_HNRPDL 0.716 TDRD9 0.752 0.938 0.002 0 BMX_HNRPDL 0.716 GPR84 0.71 0.904 0.006 0 BMX_HNRPDL 0.716 PLB1 0.722 0.884 0 0.021 BMX_HNRPDL 0.716 GSR 0.605 0.805 0.012 0.035 BMX_HNRPDL 0.716 SMPDL3A 0.736 0.9 0.001 0.008 BMX_HNRPDL 0.716 PDGFC 0.822 0.936 0 0.033 BMX_HNRPDL 0.716 EXOSC4 0.742 0.918 0 0.015 BMX_HNRPDL 0.716 DNAJC9_FAM149B1 0.742 0.835 0.033 0.036 BMX_HNRPDL 0.716 ABCA13 0.699 0.839 0.014 0.026 BMX_HNRPDL 0.716 PCOLCE2 0.667 0.857 0.002 0.024 BMX_HNRPDL 0.716 MKI67 0.674 0.839 0.007 0.032 BMX_HNRPDL 0.716 RETN 0.473 0.828 0.019 0 BMX_HNRPDL 0.716 MACF1 0.699 0.836 0.018 0.033 BMX_HNRPDL 0.716 GALNT2 0.673 0.864 0 0.024 BMX_HNRPDL 0.716 KIAA0746 0.713 0.857 0.012 0.018 BMX_HNRPDL 0.716 LHFP 0.561 0.798 0.02 0.009 BMX_HNRPDL 0.716 IGK@_IGKC_IGKV 0.774 0.87 0.007 0.026 BMX_HNRPDL 0.716 TAAR1 0.589 0.806 0.047 0.006 BMX_HNRPDL 0.716 IGLV6-57 0.757 0.867 0.004 0.043 STOM 0.792 CLEC4E 0.849 0.922 0.033 0.005 STOM 0.792 LOC284757 0.921 0.958 0.003 0.03 STOM 0.792 PPP2R5A_SNORA16 0.716 0.908 0.044 0 STOM 0.792 PRR13_PCBP2 0.758 0.926 0.016 0.001 STOM 0.792 GAB2 0.842 0.919 0.012 0.02 STOM 0.792 AREG 0.814 0.92 0.01 0.008 STOM 0.792 IRS2 0.851 0.906 0.045 0.044 STOM 0.792 CCDC125 0.79 0.912 0.038 0.005 STOM 0.792 MME 0.847 0.887 0.047 0.04 TDRD9 0.752 IL18R1 0.748 0.895 0.01 0.008 TDRD9 0.752 MMP9_LOC1001280 0.729 0.906 0 0.026 TDRD9 0.752 GK3P_GK 0.654 0.874 0.015 0.001 TDRD9 0.752 PFKFB2 0.777 0.942 0 0.007 TDRD9 0.752 ODZ1 0.777 0.914 0.002 0.007 TDRD9 0.752 SH3PXD2B 0.751 0.894 0.004 0.017 TDRD9 0.752 HMGB2 0.673 0.835 0.03 0.025 TDRD9 0.752 TPST1 0.754 0.86 0.026 0.032 TDRD9 0.752 THBS1 0.698 0.837 0.041 0.029 KREMEN1 0.835 PRR13_PCBP2 0.758 0.926 0.013 0.012 KREMEN1 0.835 SLC11A1 0.705 0.9 0.018 0.011 KREMEN1 0.835 KDM6B_TMEM88 0.672 0.919 0.017 0.002 CLEC4E 0.849 DNAJC13 0.772 0.921 0.03 0.005 CLEC4E 0.849 FOXD4L3_FOXD4L6 0.861 0.951 0.012 0.015 CLEC4E 0.849 SLC15A2 0.817 0.913 0.043 0.039 CLEC4E 0.849 VAMP2 0.595 0.911 0.012 0 CLEC4E 0.849 CLU 0.784 0.921 0.017 0.009 CLEC4E 0.849 CD63 0.813 0.956 0.001 0.011 CLEC4E 0.849 DDAH2 0.809 0.942 0.003 0.029 CLEC4E 0.849 PDGFC 0.822 0.958 0.005 0.006 CLEC4E 0.849 LRRC70_IPO11 0.886 0.952 0.012 0.033 CLEC4E 0.849 EXOSC4 0.742 0.934 0.012 0.002 CLEC4E 0.849 HIST1H3J 0.829 0.955 0.006 0.009 CLEC4E 0.849 SUCNR1 0.861 0.942 0.024 0.008 CLEC4E 0.849 AIG1 0.873 0.962 0.001 0.039 CLEC4E 0.849 PCOLCE2 0.667 0.901 0.037 0.001 CLEC4E 0.849 HS2ST1_UBA2 0.837 0.942 0.005 0.032 CLEC4E 0.849 HIST1H3A 0.822 0.947 0.009 0.008 CLEC4E 0.849 GSTO1 0.83 0.939 0.005 0.026 CLEC4E 0.849 ACTA2 0.818 0.946 0.006 0.015 CLEC4E 0.849 DPH3 0.706 0.932 0.038 0 CLEC4E 0.849 TYMS 0.838 0.944 0.004 0.026 CLEC4E 0.849 ITGA2B 0.752 0.924 0.038 0.001 CLEC4E 0.849 HIST1H3B 0.829 0.936 0.038 0.01 CLEC4E 0.849 PTGS1 0.794 0.94 0.01 0.003 CLEC4E 0.849 HIST1H3C 0.854 0.951 0.023 0.004 CLEC4E 0.849 CD151 0.767 0.921 0.046 0.003 CLEC4E 0.849 HIST1H3H 0.836 0.932 0.035 0.02 CLEC4E 0.849 FSD1L 0.834 0.937 0.015 0.01 CLEC4E 0.849 TPX2 0.745 0.928 0.021 0.002 CLEC4E 0.849 APOLD1 0.777 0.911 0.03 0.014 CLEC4E 0.849 HIST1H2BJ 0.797 0.941 0.031 0.004 CLEC4E 0.849 SON 0.794 0.921 0.024 0.02 CLEC4E 0.849 LASS4 0.669 0.902 0.035 0.002 CLEC4E 0.849 B4GALT3 0.785 0.917 0.029 0.015 CLEC4E 0.849 TNFRSF17 0.827 0.932 0.028 0.008 CLEC4E 0.849 CMTM5 0.814 0.957 0.008 0.002 CLEC4E 0.849 IGL@_IGLV1-44_(—) 0.77 0.922 0.022 0.005 CLEC4E 0.849 GLDC 0.775 0.939 0.002 0.005 CLEC4E 0.849 KIAA0101_CSNK1G 0.87 0.948 0.014 0.025 IL18R1 0.748 HPGD 0.655 0.874 0.027 0.001 IL18R1 0.748 FOLR3_FOLR2 0.585 0.81 0.024 0.014 IL18R1 0.748 PCOLCE2 0.667 0.842 0.014 0.04 IL18R1 0.748 LHFP 0.561 0.8 0.045 0.01 IL18R1 0.748 TAAR1 0.589 0.834 0.024 0.004 ACER3 0.842 PRR13_PCBP2 0.758 0.911 0.034 0.009 ACER3 0.842 MTHFS 0.733 0.939 0.009 0.001 ACER3 0.842 AREG 0.814 0.906 0.023 0.04 ACER3 0.842 KDM6B_TMEM88 0.672 0.9 0.039 0.001 ERLIN1 0.756 GSR 0.605 0.819 0.043 0.013 ERLIN1 0.756 SMPDL3A 0.736 0.859 0.036 0.01 ERLIN1 0.756 EXOSC4 0.742 0.876 0.004 0.039 ERLIN1 0.756 EFCAB2 0.784 0.87 0.049 0.016 ERLIN1 0.756 PCOLCE2 0.667 0.855 0.017 0.005 ERLIN1 0.756 GALNT2 0.673 0.86 0.008 0.012 ERLIN1 0.756 HDHD1A 0.7 0.841 0.022 0.035 ERLIN1 0.756 DPH3 0.706 0.845 0.035 0.03 ERLIN1 0.756 ANKRD28 0.71 0.851 0.03 0.017 TGFBR1 0.874 LOC284757 0.921 0.977 0.025 0.02 TGFBR1 0.874 RUNX2 0.868 0.952 0.024 0.029 TGFBR1 0.874 MPZL3 0.889 0.969 0.016 0.011 TGFBR1 0.874 SYNE2 0.886 0.974 0.029 0.005 TGFBR1 0.874 RGS2 0.789 0.965 0.028 0 FKBP5_LOC285847 0.79 C7orf53 0.711 0.882 0.041 0.004 FKBP5_LOC285847 0.79 PLB1 0.722 0.908 0.015 0.002 FKBP5_LOC285847 0.79 DNAJC13 0.772 0.913 0.004 0.017 FKBP5_LOC285847 0.79 GSR 0.605 0.866 0.034 0.002 FKBP5_LOC285847 0.79 METTL7B 0.749 0.874 0.042 0.018 FKBP5_LOC285847 0.79 VAMP2 0.595 0.853 0.048 0.002 FKBP5_LOC285847 0.79 CD63 0.813 0.935 0 0.05 FKBP5_LOC285847 0.79 PDGFC 0.822 0.948 0.001 0.013 FKBP5_LOC285847 0.79 SEC24A_SAR1B 0.739 0.87 0.032 0.031 FKBP5_LOC285847 0.79 EXOSC4 0.742 0.906 0.005 0.009 FKBP5_LOC285847 0.79 PCOLCE2 0.667 0.888 0.007 0.002 FKBP5_LOC285847 0.79 GALNT2 0.673 0.887 0.014 0.004 FKBP5_LOC285847 0.79 DPH3 0.706 0.882 0.033 0.006 FKBP5_LOC285847 0.79 ANKRD28 0.71 0.882 0.045 0.003 FKBP5_LOC285847 0.79 ITGA2B 0.752 0.878 0.039 0.017 FKBP5_LOC285847 0.79 CD151 0.767 0.881 0.029 0.043 FKBP5_LOC285847 0.79 FSD1L 0.834 0.919 0.005 0.043 FKBP5_LOC285847 0.79 HIST1H2BJ 0.797 0.897 0.029 0.034 FKBP5_LOC285847 0.79 FAM118B 0.74 0.875 0.04 0.02 FKBP5_LOC285847 0.79 PLEKHF2 0.737 0.868 0.041 0.038 GPR84 0.71 GK3P_GK 0.654 0.831 0.037 0.004 GPR84 0.71 SH3PXD2B 0.751 0.858 0.009 0.029 C7orf53 0.711 PFKFB2 0.777 0.857 0.032 0.015 C7orf53 0.711 ODZ1 0.777 0.871 0.023 0.006 C7orf53 0.711 PPP2R5A_SNORA16 0.716 0.869 0.002 0.029 C7orf53 0.711 CHI3L1 0.798 0.861 0.02 0.049 C7orf53 0.711 TPST1 0.754 0.869 0.034 0.001 C7orf53 0.711 MTHFS 0.733 0.856 0.007 0.036 C7orf53 0.711 HAL 0.846 0.945 0 0.047 C7orf53 0.711 CPM 0.812 0.905 0.001 0.031 C7orf53 0.711 PPP1R2_PPP1R2P3 0.637 0.791 0.038 0.043 PLB1 0.722 MMP9_LOC1001280 0.729 0.88 0.004 0.012 PLB1 0.722 PFKFB2 0.777 0.927 0.001 0.001 PLB1 0.722 ODZ1 0.777 0.902 0.015 0 PLB1 0.722 SH3PXD2B 0.751 0.877 0.018 0.005 PLB1 0.722 CD163 0.808 0.894 0.011 0.032 PLB1 0.722 MTHFS 0.733 0.906 0 0.014 PLB1 0.722 SLC11A1 0.705 0.864 0 0.045 DSE 0.784 MMP9_LOC1001280 0.729 0.881 0.043 0.005 DSE 0.784 PPP2R5A_SNORA16 0.716 0.894 0.021 0.002 DSE 0.784 PRR13_PCBP2 0.758 0.878 0.017 0.038 DSE 0.784 MTHFS 0.733 0.934 0.003 0.001 DSE 0.784 CDA 0.694 0.879 0.026 0.006 DSE 0.784 SLC11A1 0.705 0.875 0.025 0.004 DSE 0.784 CCDC125 0.79 0.894 0.012 0.044 DSE 0.784 KDM6B_TMEM88 0.672 0.86 0.047 0.004 DSE 0.784 RGS2 0.789 0.915 0.001 0.037 PTGDR 0.616 DNAJC9_FAM149B1 0.742 0.835 0.002 0.049 DNAJC13 0.772 PFKFB2 0.777 0.903 0.031 0.002 DNAJC13 0.772 IFI16 0.721 0.892 0.018 0.006 DNAJC13 0.772 PPP2R5A_SNORA16 0.716 0.913 0.005 0.002 DNAJC13 0.772 PRR13_PCBP2 0.758 0.891 0.01 0.014 DNAJC13 0.772 SLC11A1 0.705 0.869 0.033 0.003 DNAJC13 0.772 ERGIC1 0.758 0.894 0.014 0.003 DNAJC13 0.772 CCDC125 0.79 0.932 0.002 0.007 DNAJC13 0.772 CPM 0.812 0.907 0.021 0.007 DNAJC13 0.772 MPZL2 0.775 0.879 0.032 0.011 TNFAIP6 0.676 EIF2AK2 0.646 0.824 0.025 0.006 TNFAIP6 0.676 HIST2H2BF_HIST2 0.62 0.773 0.046 0.041 FOXD4L3_FOXD4L6 0.861 PPP2R5A_SNORA16 0.716 0.93 0.022 0.001 FOXD4L3_FOXD4L6 0.861 MTHFS 0.733 0.919 0.049 0.003 FOXD4L3_FOXD4L6 0.861 SAP30 0.789 0.926 0.008 0.017 FOXD4L3_FOXD4L6 0.861 AREG 0.814 0.914 0.041 0.034 FOXD4L3_FOXD4L6 0.861 HAL 0.846 0.945 0.006 0.044 FOXD4L3_FOXD4L6 0.861 CCDC125 0.79 0.921 0.032 0.021 FOXD4L3_FOXD4L6 0.861 ZRANB1 0.718 0.91 0.049 0.003 FOXD4L3_FOXD4L6 0.861 RGS2 0.789 0.926 0.003 0.035 FOXD4L3_FOXD4L6 0.861 CAMK1D 0.735 0.923 0.02 0.003 MMP9_LOC1001280 0.729 METTL7B 0.749 0.875 0.016 0.011 MMP9_LOC1001280 0.729 SLC15A2 0.817 0.914 0.001 0.042 MMP9_LOC1001280 0.729 SMPDL3A 0.736 0.89 0.012 0.003 MMP9_LOC1001280 0.729 CD63 0.813 0.949 0 0.016 MMP9_LOC1001280 0.729 DDAH2 0.809 0.933 0 0.025 MMP9_LOC1001280 0.729 ATP13A3 0.796 0.898 0.01 0.017 MMP9_LOC1001280 0.729 PDGFC 0.822 0.94 0 0.017 MMP9_LOC1001280 0.729 SEC24A_SAR1B 0.739 0.882 0.006 0.013 MMP9_LOC1001280 0.729 EXOSC4 0.742 0.911 0.002 0.003 MMP9_LOC1001280 0.729 AP3B2 0.697 0.843 0.046 0.011 MMP9_LOC1001280 0.729 SUCNR1 0.861 0.918 0.003 0.021 MMP9_LOC1001280 0.729 AIG1 0.873 0.955 0 0.039 MMP9_LOC1001280 0.729 PCOLCE2 0.667 0.886 0.001 0.004 MMP9_LOC1001280 0.729 GALNT2 0.673 0.906 0 0.002 MMP9_LOC1001280 0.729 ITGA2B 0.752 0.863 0.029 0.015 MMP9_LOC1001280 0.729 LHFP 0.561 0.832 0.046 0 MMP9_LOC1001280 0.729 LAIR1_LAIR2 0.777 0.887 0.011 0.016 MMP9_LOC1001280 0.729 HIST1H3B 0.829 0.912 0.002 0.047 MMP9_LOC1001280 0.729 PTGS1 0.794 0.881 0.005 0.049 MMP9_LOC1001280 0.729 CD151 0.767 0.899 0.002 0.011 MMP9_LOC1001280 0.729 FSD1L 0.834 0.914 0.003 0.03 MMP9_LOC1001280 0.729 TPX2 0.745 0.855 0.019 0.027 MMP9_LOC1001280 0.729 IGK@_IGKC_IGKV 0.774 0.873 0.036 0.004 MMP9_LOC1001280 0.729 HIST1H2BJ 0.797 0.874 0.013 0.047 MMP9_LOC1001280 0.729 TNFRSF17 0.827 0.921 0.002 0.01 MMP9_LOC1001280 0.729 IGLV6-57 0.757 0.881 0.019 0.002 MMP9_LOC1001280 0.729 IGL@_IGLV1-44_(—) 0.77 0.883 0.006 0.021 GSR 0.605 PFKFB2 0.777 0.861 0.003 0.005 GSR 0.605 ODZ1 0.777 0.819 0.021 0.027 GSR 0.605 CD163 0.808 0.849 0.01 0.015 GSR 0.605 THBS1 0.698 0.771 0.046 0.033 KLRF1 0.635 IGJ 0.683 0.81 0.01 0.015 GK3P_GK 0.654 C1orf161 0.577 0.754 0.031 0.042 GK3P_GK 0.654 HPGD 0.655 0.797 0.039 0.01 GK3P_GK 0.654 TAAR1 0.589 0.763 0.028 0.026 PFKFB2 0.777 METTL7B 0.749 0.875 0.009 0.03 PFKFB2 0.777 VAMP2 0.595 0.833 0.019 0.009 PFKFB2 0.777 SMPDL3A 0.736 0.894 0.018 0.004 PFKFB2 0.777 PDGFC 0.822 0.958 0.001 0.01 PFKFB2 0.777 LRRC70_IPO11 0.886 0.965 0 0.048 PFKFB2 0.777 SEC24A_SAR1B 0.739 0.881 0.003 0.031 PFKFB2 0.777 EXOSC4 0.742 0.94 0.001 0.003 PFKFB2 0.777 FOLR3_FOLR2 0.585 0.855 0.034 0.001 PFKFB2 0.777 AP3B2 0.697 0.859 0.037 0.019 PFKFB2 0.777 ABCA13 0.699 0.867 0.043 0.009 PFKFB2 0.777 EFCAB2 0.784 0.887 0.041 0.012 PFKFB2 0.777 PCOLCE2 0.667 0.913 0.002 0.001 PFKFB2 0.777 MKI67 0.674 0.861 0.03 0.005 PFKFB2 0.777 RETN 0.473 0.868 0.026 0 PFKFB2 0.777 IRF4 0.74 0.844 0.012 0.034 PFKFB2 0.777 MACF1 0.699 0.879 0.015 0.007 PFKFB2 0.777 GALNT2 0.673 0.911 0.002 0.002 PFKFB2 0.777 HDHD1A 0.7 0.855 0.025 0.016 PFKFB2 0.777 DPH3 0.706 0.874 0.013 0.015 PFKFB2 0.777 KIAA0746 0.713 0.876 0.024 0.005 PFKFB2 0.777 LHFP 0.561 0.848 0.034 0 PFKFB2 0.777 LAIR1_LAIR2 0.777 0.9 0.005 0.044 PFKFB2 0.777 FSD1L 0.834 0.932 0.003 0.019 PFKFB2 0.777 TPX2 0.745 0.873 0.009 0.046 PFKFB2 0.777 IGK@_IGKC_IGKV 0.774 0.871 0.015 0.047 PFKFB2 0.777 LASS4 0.669 0.835 0.015 0.027 PFKFB2 0.777 IGLV6-57 0.757 0.881 0.015 0.015 PICALM 0.758 ECHDC3 0.823 0.894 0.037 0.005 PICALM 0.758 PPP2R5A_SNORA16 0.716 0.882 0.041 0.001 PICALM 0.758 PRR13_PCBP2 0.758 0.862 0.046 0.03 PICALM 0.758 SLC11A1 0.705 0.861 0.038 0.01 PICALM 0.758 PDE3B 0.861 0.95 0.001 0.017 PICALM 0.758 ERGIC1 0.758 0.87 0.026 0.021 PICALM 0.758 NA 0.884 0.943 0.002 0.035 PICALM 0.758 HAL 0.846 0.958 0.001 0.004 PICALM 0.758 KIAA1257_ACAD9/ 0.823 0.906 0.005 0.038 PICALM 0.758 SYNE2 0.886 0.958 0.001 0.033 PICALM 0.758 MME 0.847 0.93 0.013 0.003 PICALM 0.758 RBP7 0.803 0.913 0.023 0.006 PICALM 0.758 RGS2 0.789 0.973 0 0.001 PICALM 0.758 CAMK1D 0.735 0.859 0.039 0.027 METTL7B 0.749 PPP2R5A_SNORA16 0.716 0.86 0.006 0.018 METTL7B 0.749 MTHFS 0.733 0.861 0.023 0.016 METTL7B 0.749 CDA 0.694 0.861 0.007 0.015 METTL7B 0.749 SLC11A1 0.705 0.848 0.026 0.011 METTL7B 0.749 ERGIC1 0.758 0.853 0.017 0.038 METTL7B 0.749 CAMK1D 0.735 0.835 0.007 0.036 HIST1H4C 0.881 RUNX2 0.868 0.969 0.004 0.027 HIST1H4C 0.881 AREG 0.814 0.936 0.038 0.01 HIST1H4C 0.881 PDE3B 0.861 0.973 0.012 0.006 HIST1H4C 0.881 OCR1 0.934 0.979 0.014 0.049 HIST1H4C 0.881 NA 0.884 0.984 0.008 0.003 HIST1H4C 0.881 HAL 0.846 0.945 0.042 0.016 HIST1H4C 0.881 CCDC125 0.79 0.945 0.044 0.004 HIST1H4C 0.881 SYNE2 0.886 0.971 0.011 0.013 HIST1H4C 0.881 RGS2 0.789 0.944 0.021 0.008 C9orf72 0.843 NA 0.884 0.951 0.009 0.042 C9orf72 0.843 HAL 0.846 0.946 0.024 0.007 C9orf72 0.843 KIAA1257_ACAD9/ 0.823 0.939 0.02 0.006 C9orf72 0.843 RGS2 0.789 0.97 0.005 0.001 HIST1H3I 0.895 PPP2R5A_SNORA16 0.716 0.951 0.05 0 HIST1H3I 0.895 RUNX2 0.868 0.955 0.023 0.039 HIST1H3I 0.895 OCR1 0.934 0.984 0.015 0.042 HIST1H3I 0.895 NA 0.884 0.995 0.008 0.003 HIST1H3I 0.895 SYNE2 0.886 0.977 0.014 0.014 SLC15A2 0.817 PPP2R5A_SNORA16 0.716 0.906 0.033 0.001 SLC15A2 0.817 PRR13_PCBP2 0.758 0.894 0.043 0.013 SLC15A2 0.817 AREG 0.814 0.897 0.041 0.029 SLC15A2 0.817 CCDC125 0.79 0.93 0.015 0.009 SLC15A2 0.817 SYNE2 0.886 0.959 0.002 0.03 SLC15A2 0.817 RGS2 0.789 0.895 0.013 0.035 SLC15A2 0.817 LOC100128751 0.809 0.934 0.017 0.01 TLR10 0.884 CASS4 0.732 0.936 0.035 0.001 TLR10 0.884 PPP2R5A_SNORA16 0.716 0.951 0.039 0 TLR10 0.884 HIST1H2AA 0.825 0.932 0.031 0.032 TLR10 0.884 PDE3B 0.861 0.965 0.008 0.03 TLR10 0.884 ERGIC1 0.758 0.945 0.033 0.002 TLR10 0.884 NA 0.884 0.96 0.008 0.05 TLR10 0.884 HAL 0.846 0.974 0.013 0.006 TLR10 0.884 KIAA1257_ACAD9/ 0.823 0.958 0.018 0.009 TLR10 0.884 ZRANB1 0.718 0.94 0.036 0 TLR10 0.884 SYNE2 0.886 0.962 0.013 0.047 TLR10 0.884 MME 0.847 0.963 0.039 0.004 TLR10 0.884 RBP7 0.803 0.958 0.048 0.002 TLR10 0.884 RGS2 0.789 0.971 0.003 0.004 TLR10 0.884 CAMK1D 0.735 0.936 0.039 0.001 ADM 0.888 LOC284757 0.921 0.968 0.031 0.032 ADM 0.888 PRR13_PCBP2 0.758 0.963 0.037 0 ADM 0.888 MTHFS 0.733 0.978 0.016 0 ADM 0.888 GAB2 0.842 0.959 0.013 0.012 ADM 0.888 AREG 0.814 0.959 0.032 0.003 ADM 0.888 MPZL3 0.889 0.975 0.006 0.022 ADM 0.888 KDM6B_TMEM88 0.672 0.953 0.045 0 CD274 0.926 IFI16 0.721 0.961 0.022 0 CD274 0.926 NA 0.884 0.968 0.018 0.031 CRIP1 0.833 CASS4 0.732 0.881 0.035 0.023 CRIP1 0.833 PPP2R5A_SNORA16 0.716 0.888 0.027 0.018 CRIP1 0.833 ZRANB1 0.718 0.888 0.037 0.014 CRIP1 0.833 RASA4_RASA4P_R 0.713 0.881 0.033 0.027 CRIP1 0.833 CAMK1D 0.735 0.928 0.006 0.005 VAMP2 0.595 TPST1 0.754 0.804 0.022 0.044 VAMP2 0.595 SAP30 0.789 0.871 0 0.023 VAMP2 0.595 ACPL2 0.756 0.822 0.01 0.03 VAMP2 0.595 CCDC125 0.79 0.863 0.001 0.036 SMPDL3A 0.736 SLC1A3 0.777 0.88 0.007 0.04 SMPDL3A 0.736 PPP2R5A_SNORA16 0.716 0.868 0.002 0.024 SMPDL3A 0.736 MTHFS 0.733 0.882 0.003 0.024 SMPDL3A 0.736 CDA 0.694 0.87 0.001 0.025 SMPDL3A 0.736 SLC11A1 0.705 0.835 0.009 0.027 SMPDL3A 0.736 ACPL2 0.756 0.844 0.02 0.048 IFI16 0.721 SEC24A_SAR1B 0.739 0.862 0.007 0.035 IFI16 0.721 CCR1 0.72 0.874 0.026 0.003 IFI16 0.721 FFAR2 0.744 0.895 0.006 0.004 IFI16 0.721 TRIM21 0.857 0.95 0 0.045 IFI16 0.721 UBE2F_C20orf194 0.758 0.857 0.042 0.017 IFI16 0.721 DYNLL1 0.787 0.864 0.024 0.038 IFI16 0.721 CTSL1_CTSLL3 0.819 0.899 0.009 0.029 MRPL41 0.845 CASS4 0.732 0.906 0.034 0.003 MRPL41 0.845 PPP2R5A_SNORA16 0.716 0.932 0.016 0.001 MRPL41 0.845 AREG 0.814 0.896 0.043 0.048 MRPL41 0.845 SFRS9 0.523 0.895 0.013 0 MRPL41 0.845 HAL 0.846 0.934 0.012 0.029 MRPL41 0.845 KIAA1257_ACAD9/ 0.823 0.923 0.021 0.036 MRPL41 0.845 CCDC125 0.79 0.936 0.003 0.017 MRPL41 0.845 SYNE2 0.886 0.98 0.002 0.012 MRPL41 0.845 MME 0.847 0.946 0.027 0.006 MRPL41 0.845 RBP7 0.803 0.974 0.005 0.001 MRPL41 0.845 RGS2 0.789 0.923 0.007 0.039 MRPL41 0.845 CAMK1D 0.735 0.919 0.014 0.004 SLC1A3 0.777 PDGFC 0.822 0.926 0.007 0.004 SLC1A3 0.777 EXOSC4 0.742 0.894 0.002 0.029 SLC1A3 0.777 PCOLCE2 0.667 0.923 0.005 0 SLC1A3 0.777 GALNT2 0.673 0.898 0.002 0.003 CASS4 0.732 CLU 0.784 0.863 0.012 0.041 CASS4 0.732 KPNA5 0.793 0.862 0.037 0.031 CASS4 0.732 FFAR2 0.744 0.846 0.015 0.017 CASS4 0.732 LGALS2 0.77 0.881 0.008 0.005 CASS4 0.732 EFCAB2 0.784 0.865 0.022 0.012 CASS4 0.732 HINT1 0.819 0.882 0.015 0.015 CASS4 0.732 HIST1H3A 0.822 0.891 0.005 0.031 CASS4 0.732 PMS2CL_PMS2 0.857 0.932 0 0.045 CASS4 0.732 NEK6_LOC1001290 0.844 0.928 0 0.047 CASS4 0.732 RPL17_SNORD58B 0.842 0.908 0.004 0.028 CASS4 0.732 DLEU2_DLEU2L 0.758 0.876 0.007 0.011 CASS4 0.732 IL1B 0.769 0.867 0.015 0.009 CASS4 0.732 ITGA2B 0.752 0.844 0.05 0.016 CASS4 0.732 PTGS1 0.794 0.887 0.004 0.014 CASS4 0.732 HIST1H3C 0.854 0.897 0.009 0.047 CASS4 0.732 E2F6 0.798 0.882 0.019 0.018 CASS4 0.732 CD151 0.767 0.85 0.038 0.043 CASS4 0.732 HIST1H3H 0.836 0.895 0.009 0.019 CASS4 0.732 TPX2 0.745 0.852 0.024 0.033 CASS4 0.732 APOLD1 0.777 0.9 0.003 0.012 CASS4 0.732 HIST1H2BJ 0.797 0.876 0.024 0.011 CASS4 0.732 C7orf58 0.787 0.879 0.012 0.006 CASS4 0.732 DYNLL1 0.787 0.888 0.008 0.006 CASS4 0.732 B4GALT3 0.785 0.89 0.006 0.018 CASS4 0.732 NP 0.787 0.883 0.008 0.015 CASS4 0.732 CMTM5 0.814 0.882 0.008 0.042 CASS4 0.732 AIF1 0.745 0.87 0.012 0.011 CASS4 0.732 NFXL1 0.808 0.907 0.002 0.013 CLU 0.784 PPP2R5A_SNORA16 0.716 0.903 0.015 0.002 CLU 0.784 PRR13_PCBP2 0.758 0.905 0.014 0.003 CLU 0.784 SULF2 0.775 0.91 0.012 0.007 CLU 0.784 OCR1 0.934 0.966 0.001 0.043 CLU 0.784 ERGIC1 0.758 0.882 0.033 0.017 CLU 0.784 HAL 0.846 0.946 0.001 0.01 CLU 0.784 KIAA1257_ACAD9/ 0.823 0.92 0.004 0.021 CLU 0.784 CCDC125 0.79 0.919 0.008 0.007 CLU 0.784 ZRANB1 0.718 0.913 0.006 0.001 CLU 0.784 MME 0.847 0.922 0.009 0.025 CLU 0.784 CPM 0.812 0.912 0.034 0.002 CLU 0.784 RBP7 0.803 0.91 0.017 0.007 CLU 0.784 RGS2 0.789 0.903 0.006 0.033 CLU 0.784 CAMK1D 0.735 0.863 0.048 0.012 CLU 0.784 MPZL2 0.775 0.899 0.02 0.006 CLU 0.784 LOC100128751 0.809 0.895 0.014 0.045 ODZ1 0.777 ANAPC11 0.705 0.837 0.05 0.045 ODZ1 0.777 DNAJC9_FAM149B1 0.742 0.868 0.035 0.027 ODZ1 0.777 TREML1 0.704 0.868 0.038 0.01 ODZ1 0.777 MACF1 0.699 0.861 0.035 0.011 ODZ1 0.777 GALNT2 0.673 0.837 0.011 0.048 ODZ1 0.777 ANKRD28 0.71 0.87 0.02 0.007 ODZ1 0.777 KIAA0746 0.713 0.861 0.012 0.023 ODZ1 0.777 ITGA2B 0.752 0.892 0.002 0.031 ODZ1 0.777 MYL9 0.695 0.872 0.007 0.015 ODZ1 0.777 PLB1 0.609 0.841 0.036 0.003 KPNA5 0.793 PPP2R5A_SNORA16 0.716 0.853 0.047 0.044 KPNA5 0.793 ZRANB1 0.718 0.882 0.016 0.011 KPNA5 0.793 PYHIN1 0.647 0.919 0.026 0 KPNA5 0.793 PPP1R2_PPP1R2P3 0.637 0.871 0.02 0.003 CD63 0.813 LOC284757 0.921 0.984 0.001 0.015 CD63 0.813 PPP2R5A_SNORA16 0.716 0.924 0.028 0 CD63 0.813 RUNX2 0.868 0.953 0.003 0.019 CD63 0.813 MTHFS 0.733 0.936 0.034 0 CD63 0.813 HIST1H2AA 0.825 0.917 0.016 0.013 CD63 0.813 CDA 0.694 0.936 0.02 0 CD63 0.813 SAP30 0.789 0.919 0.032 0.002 CD63 0.813 GAB2 0.842 0.942 0.003 0.033 CD63 0.813 SLC11A1 0.705 0.965 0.003 0 CD63 0.813 AREG 0.814 0.952 0.002 0.003 CD63 0.813 PDE3B 0.861 0.945 0.018 0.009 CD63 0.813 IRS2 0.851 0.962 0.007 0.002 CD63 0.813 ERGIC1 0.758 0.947 0.013 0 CD63 0.813 NA 0.884 0.924 0.019 0.039 CD63 0.813 HAL 0.846 0.945 0.012 0.006 CD63 0.813 KIAA1257_ACAD9/ 0.823 0.927 0.032 0.005 CD63 0.813 CCDC125 0.79 0.93 0.026 0.004 CD63 0.813 SYNE2 0.886 0.94 0.01 0.036 CD63 0.813 RGS2 0.789 0.929 0.014 0.004 HPSE 0.894 PPP2R5A_SNORA16 0.716 0.966 0.026 0 HPSE 0.894 RUNX2 0.868 0.95 0.04 0.02 HPSE 0.894 GAB2 0.842 0.965 0.022 0.003 HPSE 0.894 OCR1 0.934 0.977 0.02 0.038 HPSE 0.894 NA 0.884 0.975 0.016 0.007 HPSE 0.894 MPZL3 0.889 0.963 0.011 0.037 HPSE 0.894 SYNE2 0.886 0.969 0.02 0.01 HPSE 0.894 RGS2 0.789 0.96 0.018 0.001 C1orf161 0.577 OLAH 0.706 0.778 0.05 0.004 C1orf161 0.577 CD163 0.808 0.875 0.001 0.03 DDAH2 0.809 PRR13_PCBP2 0.758 0.935 0.018 0 DDAH2 0.809 CDA 0.694 0.95 0.011 0 DDAH2 0.809 GAB2 0.842 0.933 0.008 0.022 DDAH2 0.809 SLC11A1 0.705 0.932 0.026 0 DDAH2 0.809 AREG 0.814 0.936 0.006 0.005 DDAH2 0.809 IRS2 0.851 0.921 0.046 0.019 DDAH2 0.809 RGS2 0.789 0.907 0.044 0.007 ATP13A3 0.796 PPP2R5A_SNORA16 0.716 0.921 0.001 0.004 ATP13A3 0.796 MTHFS 0.733 0.915 0.002 0.01 ATP13A3 0.796 CDA 0.694 0.88 0.014 0.015 ATP13A3 0.796 CCDC125 0.79 0.915 0.004 0.039 PMAIP1 0.945 RUNX2 0.868 0.974 0.029 0.018 LOC284757 0.921 PDGFC 0.822 0.98 0.036 0.002 LOC284757 0.921 B3GAT3 0.857 0.977 0.013 0.013 LOC284757 0.921 RCBTB2_LOC10013 0.882 0.966 0.01 0.046 LOC284757 0.921 SLC39A9 0.837 0.968 0.008 0.014 LOC284757 0.921 LGALS1 0.855 0.965 0.03 0.016 LOC284757 0.921 AIG1 0.873 0.975 0.034 0.012 LOC284757 0.921 PCOLCE2 0.667 0.964 0.039 0 LOC284757 0.921 SLC39A8 0.867 0.984 0.032 0.01 LOC284757 0.921 GSTO1 0.83 0.971 0.041 0.004 LOC284757 0.921 PTGER2 0.842 0.979 0.026 0.007 LOC284757 0.921 PMS2CL_PMS2 0.857 0.957 0.024 0.016 LOC284757 0.921 NEK6_LOC1001290 0.844 0.976 0.01 0.009 LOC284757 0.921 GLT25D1 0.808 0.977 0.016 0.001 LOC284757 0.921 ICAM1 0.895 0.975 0.019 0.032 LOC284757 0.921 P4HA1_RPL17 0.88 0.961 0.037 0.04 LOC284757 0.921 RRP12_LOC644215 0.84 0.973 0.019 0.007 LOC284757 0.921 LAIR1_LAIR2 0.777 0.966 0.034 0.001 LOC284757 0.921 FSD1L_GARNL1 0.844 0.981 0.023 0.008 LOC284757 0.921 FSD1L 0.834 0.977 0.033 0.001 LOC284757 0.921 MPO 0.684 0.954 0.043 0 LOC284757 0.921 ATP6V0D1_LOC100 0.828 0.955 0.027 0.025 PDGFC 0.822 ECHDC3 0.823 0.926 0.031 0.011 PDGFC 0.822 PPP2R5A_SNORA16 0.716 0.939 0.006 0.001 PDGFC 0.822 HMGB2 0.673 0.942 0.033 0 PDGFC 0.822 RUNX2 0.868 0.962 0.001 0.049 PDGFC 0.822 PRR13_PCBP2 0.758 0.906 0.028 0.007 PDGFC 0.822 CD163 0.808 0.938 0.019 0.008 PDGFC 0.822 MTHFS 0.733 0.914 0.029 0.002 PDGFC 0.822 CDA 0.694 0.927 0.015 0.001 PDGFC 0.822 SAP30 0.789 0.92 0.008 0.013 PDGFC 0.822 SLC11A1 0.705 0.93 0.017 0 PDGFC 0.822 AREG 0.814 0.922 0.01 0.014 PDGFC 0.822 IRS2 0.851 0.948 0.01 0.018 PDGFC 0.822 ACPL2 0.756 0.943 0.023 0 PDGFC 0.822 ERGIC1 0.758 0.933 0.017 0.001 PDGFC 0.822 HAL 0.846 0.943 0.01 0.022 PDGFC 0.822 KIAA1257_ACAD9/ 0.823 0.929 0.025 0.015 PDGFC 0.822 CCDC125 0.79 0.952 0.005 0.006 PDGFC 0.822 MME 0.847 0.94 0.036 0.003 PDGFC 0.822 CPM 0.812 0.915 0.047 0.011 PDGFC 0.822 RBP7 0.803 0.937 0.017 0.007 PDGFC 0.822 RGS2 0.789 0.944 0.005 0.007 B3GAT3 0.857 RUNX2 0.868 0.945 0.022 0.022 B3GAT3 0.857 PDE3B 0.861 0.957 0.014 0.009 B3GAT3 0.857 NA 0.884 0.962 0.025 0.004 B3GAT3 0.857 HAL 0.846 0.934 0.048 0.009 B3GAT3 0.857 KIAA1257_ACAD9/ 0.823 0.932 0.045 0.018 B3GAT3 0.857 CCDC125 0.79 0.946 0.034 0.004 B3GAT3 0.857 SYNE2 0.886 0.953 0.016 0.041 HPGD 0.655 OLAH 0.706 0.834 0.01 0.03 HPGD 0.655 IL1RL1 0.655 0.812 0.008 0.026 LRRC70_IPO11 0.886 ECHDC3 0.823 0.958 0.032 0.004 LRRC70_IPO11 0.886 MTHFS 0.733 0.938 0.04 0.001 LRRC70_IPO11 0.886 HIST1H2AA 0.825 0.943 0.014 0.021 LRRC70_IPO11 0.886 SAP30 0.789 0.939 0.017 0.009 LRRC70_IPO11 0.886 SLC11A1 0.705 0.942 0.012 0.001 LRRC70_IPO11 0.886 AREG 0.814 0.96 0.01 0.004 LRRC70_IPO11 0.886 IRS2 0.851 0.946 0.021 0.033 LRRC70_IPO11 0.886 ACPL2 0.756 0.955 0.035 0 LRRC70_IPO11 0.886 ERGIC1 0.758 0.938 0.017 0.004 LRRC70_IPO11 0.886 NA 0.884 0.95 0.032 0.042 LRRC70_IPO11 0.886 KIAA1257_ACAD9/ 0.823 0.946 0.018 0.012 LRRC70_IPO11 0.886 RGS2 0.789 0.937 0.008 0.009 TMEM144_LOC2855 0.653 PLA2G7 0.738 0.857 0.003 0.023 TMEM144_LOC2855 0.653 PDK4 0.598 0.782 0.05 0.009 CDS2 0.944 RUNX2 0.868 0.987 0.05 0.004 CDS2 0.944 MPZL3 0.889 0.995 0.023 0.005 ECHDC3 0.823 EXOSC4 0.742 0.889 0.048 0.017 ECHDC3 0.823 HIST1H3J 0.829 0.933 0.003 0.039 ECHDC3 0.823 HIST1H3A 0.822 0.929 0.005 0.028 ECHDC3 0.823 DPH3 0.706 0.922 0.008 0.001 ECHDC3 0.823 DLEU2_DLEU2L 0.758 0.902 0.029 0.015 ECHDC3 0.823 ANKRD28 0.71 0.914 0.05 0 ECHDC3 0.823 HIST1H3B 0.829 0.926 0.018 0.037 ECHDC3 0.823 PTGS1 0.794 0.915 0.022 0.015 ECHDC3 0.823 HIST1H3C 0.854 0.948 0.007 0.015 ECHDC3 0.823 HIST1H3H 0.836 0.926 0.022 0.024 ECHDC3 0.823 TPX2 0.745 0.884 0.031 0.041 ECHDC3 0.823 APOLD1 0.777 0.901 0.01 0.036 ECHDC3 0.823 HIST1H2BJ 0.797 0.941 0.019 0.002 ECHDC3 0.823 FAM118B 0.74 0.89 0.033 0.011 ECHDC3 0.823 CMTM5 0.814 0.939 0.008 0.012 ECHDC3 0.823 SDHC 0.85 0.928 0.01 0.036 ECHDC3 0.823 GLDC 0.775 0.902 0.003 0.048 OLAH 0.706 TAAR1 0.589 0.777 0.02 0.03 PPP2R5A_SNORA16 0.716 EAF2_HCG11_LOC 0.869 0.955 0 0.042 PPP2R5A_SNORA16 0.716 SEC24A_SAR1B 0.739 0.906 0.003 0.003 PPP2R5A_SNORA16 0.716 EXOSC4 0.742 0.905 0.002 0.003 PPP2R5A_SNORA16 0.716 HIST1H4L 0.929 0.978 0 0.046 PPP2R5A_SNORA16 0.716 CCR1 0.72 0.823 0.047 0.022 PPP2R5A_SNORA16 0.716 SIAE 0.894 0.975 0 0.024 PPP2R5A_SNORA16 0.716 HINT1 0.819 0.876 0.018 0.032 PPP2R5A_SNORA16 0.716 HIST1H3J 0.829 0.906 0.004 0.014 PPP2R5A_SNORA16 0.716 SUCNR1 0.861 0.939 0.001 0.007 PPP2R5A_SNORA16 0.716 MTRR 0.832 0.913 0.002 0.036 PPP2R5A_SNORA16 0.716 AIG1 0.873 0.975 0 0.003 PPP2R5A_SNORA16 0.716 PCOLCE2 0.667 0.835 0.011 0.012 PPP2R5A_SNORA16 0.716 HS2ST1_UBA2 0.837 0.954 0 0.013 PPP2R5A_SNORA16 0.716 HIST1H3A 0.822 0.914 0.002 0.011 PPP2R5A_SNORA16 0.716 GSTO1 0.83 0.937 0 0.014 PPP2R5A_SNORA16 0.716 IRF4 0.74 0.841 0.021 0.05 PPP2R5A_SNORA16 0.716 GALNT2 0.673 0.839 0.017 0.014 PPP2R5A_SNORA16 0.716 HDHD1A 0.7 0.887 0.006 0.002 PPP2R5A_SNORA16 0.716 EIF1AX_SCARNA9L 0.814 0.895 0.008 0.01 PPP2R5A_SNORA16 0.716 DPH3 0.706 0.888 0.012 0 PPP2R5A_SNORA16 0.716 TAF13 0.908 0.971 0 0.021 PPP2R5A_SNORA16 0.716 HIST1H2BM 0.862 0.939 0 0.029 PPP2R5A_SNORA16 0.716 IL1B 0.769 0.839 0.031 0.032 PPP2R5A_SNORA16 0.716 ITGA2B 0.752 0.874 0.019 0.005 PPP2R5A_SNORA16 0.716 HIST1H3B 0.829 0.893 0.01 0.035 PPP2R5A_SNORA16 0.716 FSD1L_GARNL1 0.844 0.943 0 0.038 PPP2R5A_SNORA16 0.716 PTGS1 0.794 0.901 0.002 0.016 PPP2R5A_SNORA16 0.716 UBE2F_C20orf194 0.758 0.886 0.018 0.002 PPP2R5A_SNORA16 0.716 HIST1H3C 0.854 0.924 0.004 0.007 PPP2R5A_SNORA16 0.716 CD151 0.767 0.919 0.001 0.004 PPP2R5A_SNORA16 0.716 HIST1H3H 0.836 0.909 0.005 0.013 PPP2R5A_SNORA16 0.716 FSD1L 0.834 0.913 0.002 0.013 PPP2R5A_SNORA16 0.716 TPX2 0.745 0.901 0.004 0.003 PPP2R5A_SNORA16 0.716 MYL9 0.695 0.866 0.027 0.001 PPP2R5A_SNORA16 0.716 HIST1H2BJ 0.797 0.913 0.008 0.001 PPP2R5A_SNORA16 0.716 C7orf58 0.787 0.861 0.03 0.019 PPP2R5A_SNORA16 0.716 DYNLL1 0.787 0.896 0.012 0.006 PPP2R5A_SNORA16 0.716 FAM118B 0.74 0.867 0.023 0.009 PPP2R5A_SNORA16 0.716 B4GALT3 0.785 0.913 0.001 0.025 PPP2R5A_SNORA16 0.716 NP 0.787 0.919 0.002 0.005 PPP2R5A_SNORA16 0.716 TNFRSF17 0.827 0.901 0.003 0.023 PPP2R5A_SNORA16 0.716 PLEKHF2 0.737 0.897 0.004 0.005 PPP2R5A_SNORA16 0.716 CMTM5 0.814 0.924 0.001 0.013 PPP2R5A_SNORA16 0.716 AIF1 0.745 0.853 0.017 0.019 PPP2R5A_SNORA16 0.716 IGLV6-57 0.757 0.874 0.017 0.005 PPP2R5A_SNORA16 0.716 IGL@_IGLV1-44_(—) 0.77 0.873 0.003 0.05 PPP2R5A_SNORA16 0.716 SDHC 0.85 0.925 0.006 0.011 PPP2R5A_SNORA16 0.716 KIAA0101_CSNK1G 0.87 0.926 0.003 0.03 EAF2_HCG11_LOC 0.869 NA 0.884 0.956 0.036 0.027 EAF2_HCG11_LOC 0.869 CCDC125 0.79 0.946 0.041 0.005 RCBTB2_LOC10013 0.882 RUNX2 0.868 0.951 0.014 0.047 RCBTB2_LOC10013 0.882 GAB2 0.842 0.952 0.029 0.007 RCBTB2_LOC10013 0.882 SYNE2 0.886 0.961 0.026 0.015 SEC24A_SAR1B 0.739 PRR13_PCBP2 0.758 0.869 0.012 0.033 SEC24A_SAR1B 0.739 MTHFS 0.733 0.873 0.024 0.007 SEC24A_SAR1B 0.739 CDA 0.694 0.861 0.016 0.014 SEC24A_SAR1B 0.739 SLC11A1 0.705 0.823 0.042 0.026 SEC24A_SAR1B 0.739 ERGIC1 0.758 0.84 0.042 0.044 SEC24A_SAR1B 0.739 CCDC125 0.79 0.912 0.003 0.014 SEC24A_SAR1B 0.739 MPZL2 0.775 0.872 0.027 0.014 SH3PXD2B 0.751 PCOLCE2 0.667 0.846 0.009 0.026 SH3PXD2B 0.751 MACF1 0.699 0.861 0.005 0.027 SH3PXD2B 0.751 GALNT2 0.673 0.868 0 0.03 SH3PXD2B 0.751 KIAA0746 0.713 0.846 0.013 0.05 HMGB2 0.673 DNAJC9_FAM149B1 0.742 0.823 0.027 0.05 HMGB2 0.673 ABCA13 0.699 0.861 0.004 0.006 HMGB2 0.673 MKI67 0.674 0.819 0.013 0.036 HMGB2 0.673 TREML1 0.704 0.829 0.018 0.024 HMGB2 0.673 DPH3 0.706 0.856 0.002 0.036 HMGB2 0.673 ANKRD28 0.71 0.845 0.006 0.022 HMGB2 0.673 LHFP 0.561 0.76 0.034 0.016 HMGB2 0.673 HIST1H3C 0.854 0.929 0 0.025 HMGB2 0.673 MYL9 0.695 0.825 0.005 0.048 HMGB2 0.673 HIST1H2BJ 0.797 0.914 0 0.018 HMGB2 0.673 TAAR1 0.589 0.794 0.045 0.002 KLRD1 0.594 IGJ 0.683 0.776 0.028 0.018 CHI3L1 0.798 LGALS2 0.77 0.9 0.002 0.028 CHI3L1 0.798 SUCNR1 0.861 0.946 0.001 0.042 CHI3L1 0.798 MKI67 0.674 0.881 0.017 0.005 CHI3L1 0.798 MINPP1 0.718 0.896 0.02 0.005 CHI3L1 0.798 ITGA2B 0.752 0.885 0.013 0.025 CHI3L1 0.798 UBE2F_C20orf194 0.758 0.887 0.006 0.046 CHI3L1 0.798 CD151 0.767 0.889 0.007 0.04 CHI3L1 0.798 TPX2 0.745 0.889 0.01 0.026 CHI3L1 0.798 MYL9 0.695 0.874 0.02 0.008 CHI3L1 0.798 HIST1H2BJ 0.797 0.932 0.002 0.017 CHI3L1 0.798 C7orf58 0.787 0.906 0.005 0.022 CHI3L1 0.798 DYNLL1 0.787 0.898 0.008 0.023 CHI3L1 0.798 TNFRSF17 0.827 0.926 0.004 0.039 CHI3L1 0.798 IGLV6-57 0.757 0.919 0.006 0.005 FRMD3 0.884 RUNX2 0.868 0.977 0.011 0.01 FRMD3 0.884 GAB2 0.842 0.958 0.033 0.006 FRMD3 0.884 OCR1 0.934 0.967 0.041 0.035 FRMD3 0.884 MPZL3 0.889 0.955 0.018 0.043 SLC39A9 0.837 RUNX2 0.868 0.972 0.006 0.003 SLC39A9 0.837 GAB2 0.842 0.933 0.024 0.008 SLC39A9 0.837 PDE3B 0.861 0.956 0.033 0.003 SLC39A9 0.837 NA 0.884 0.954 0.024 0.004 SLC39A9 0.837 CCDC125 0.79 0.953 0.032 0.001 SLC39A9 0.837 MPZL3 0.889 0.961 0.004 0.031 SLC39A9 0.837 SYNE2 0.886 0.975 0.008 0.003 SLC39A9 0.837 RGS2 0.789 0.939 0.036 0.001 EXOSC4 0.742 PRR13_PCBP2 0.758 0.893 0.003 0.012 EXOSC4 0.742 CD163 0.808 0.887 0.04 0.01 EXOSC4 0.742 MTHFS 0.733 0.95 0 0 EXOSC4 0.742 CDA 0.694 0.939 0 0 EXOSC4 0.742 SLC11A1 0.705 0.875 0.011 0.006 EXOSC4 0.742 AREG 0.814 0.907 0.001 0.041 EXOSC4 0.742 ACPL2 0.756 0.871 0.048 0.006 EXOSC4 0.742 ERGIC1 0.758 0.876 0.018 0.008 EXOSC4 0.742 CCDC125 0.79 0.898 0.005 0.031 NA 0.601 MACF1 0.699 0.804 0.005 0.035 NA 0.601 C7orf58 0.787 0.85 0.002 0.043 RUNX2 0.868 HIST1H4L 0.929 0.978 0.019 0.017 RUNX2 0.868 LGALS1 0.855 0.962 0.015 0.006 RUNX2 0.868 SIAE 0.894 0.968 0.024 0.008 RUNX2 0.868 AIG1 0.873 0.952 0.028 0.015 RUNX2 0.868 PTGER2 0.842 0.967 0.02 0.003 RUNX2 0.868 PMS2CL_PMS2 0.857 0.956 0.046 0.003 RUNX2 0.868 NEK6_LOC1001290 0.844 0.962 0.008 0.006 RUNX2 0.868 AMFR 0.874 0.983 0.011 0.005 RUNX2 0.868 CD300A 0.888 0.957 0.019 0.03 RUNX2 0.868 ZNF28 0.891 0.977 0.017 0.007 RUNX2 0.868 TAF13 0.908 0.96 0.045 0.022 RUNX2 0.868 KLHL5 0.913 0.954 0.044 0.047 RUNX2 0.868 MGST3 0.897 0.962 0.022 0.028 RUNX2 0.868 HSPB1_HSPBL2 0.886 0.953 0.035 0.022 RUNX2 0.868 TYMS 0.838 0.941 0.047 0.009 RUNX2 0.868 HIST1H2BM 0.862 0.952 0.038 0.008 RUNX2 0.868 CDC26 0.868 0.978 0.012 0.003 PRR13_PCBP2 0.758 FFAR2 0.744 0.888 0.028 0.002 PRR13_PCBP2 0.758 AIG1 0.873 0.953 0 0.034 PRR13_PCBP2 0.758 PCOLCE2 0.667 0.868 0.019 0.001 PRR13_PCBP2 0.758 HS2ST1_UBA2 0.837 0.924 0.002 0.031 PRR13_PCBP2 0.758 GSTO1 0.83 0.924 0.001 0.022 PRR13_PCBP2 0.758 GALNT2 0.673 0.861 0.043 0.002 PRR13_PCBP2 0.758 GLT25D1 0.808 0.898 0.006 0.037 PRR13_PCBP2 0.758 ACTA2 0.818 0.944 0.001 0.006 PRR13_PCBP2 0.758 LAIR1_LAIR2 0.777 0.859 0.048 0.031 PRR13_PCBP2 0.758 PTGS1 0.794 0.885 0.011 0.017 PRR13_PCBP2 0.758 UBE2F_C20orf194 0.758 0.888 0.033 0.004 PRR13_PCBP2 0.758 CD151 0.767 0.894 0.016 0.008 PRR13_PCBP2 0.758 FSD1L 0.834 0.904 0.006 0.029 PRR13_PCBP2 0.758 TPX2 0.745 0.868 0.034 0.007 PRR13_PCBP2 0.758 APOLD1 0.777 0.886 0.009 0.027 PRR13_PCBP2 0.758 HIST1H2BJ 0.797 0.901 0.012 0.015 PRR13_PCBP2 0.758 LASS4 0.669 0.823 0.047 0.009 PRR13_PCBP2 0.758 CTSL1_CTSLL3 0.819 0.874 0.041 0.038 PRR13_PCBP2 0.758 NP 0.787 0.878 0.031 0.026 PRR13_PCBP2 0.758 SDHC 0.85 0.915 0.014 0.024 PRR13_PCBP2 0.758 NFXL1 0.808 0.888 0.026 0.04 HIST1H4L 0.929 PDE3B 0.861 0.98 0.023 0.013 HIST1H4L 0.929 OCR1 0.934 0.993 0.028 0.042 HIST1H4L 0.929 NA 0.884 0.992 0.022 0.005 HIST1H4L 0.929 HAL 0.846 0.977 0.046 0.007 HIST1H4L 0.929 CCDC125 0.79 0.972 0.04 0.002 HIST1H4L 0.929 SYNE2 0.886 0.991 0.021 0.008 HIST1H4L 0.929 MME 0.847 0.985 0.045 0.002 LGALS1 0.855 NA 0.884 0.946 0.044 0.007 LGALS1 0.855 MPZL3 0.889 0.977 0.002 0.024 LGALS1 0.855 SYNE2 0.886 0.959 0.026 0.006 LGALS1 0.855 RGS2 0.789 0.969 0.008 0.001 CCR1 0.72 MTHFS 0.733 0.858 0.004 0.049 CCR1 0.72 CDA 0.694 0.822 0.013 0.044 CCR1 0.72 PLA2G7 0.738 0.874 0.027 0.001 CCR1 0.72 KDM6B_TMEM88 0.672 0.829 0.013 0.024 TPST1 0.754 ABCA13 0.699 0.866 0.01 0.015 TPST1 0.754 MKI67 0.674 0.843 0.014 0.02 TPST1 0.754 TREML1 0.704 0.867 0.013 0.014 TPST1 0.754 HDHD1A 0.7 0.844 0.011 0.047 TPST1 0.754 DPH3 0.706 0.871 0.003 0.031 TPST1 0.754 ANKRD28 0.71 0.878 0.006 0.017 TPST1 0.754 ITGA2B 0.752 0.884 0.001 0.041 TPST1 0.754 MYL9 0.695 0.878 0.002 0.015 TPST1 0.754 HIST1H2BJ 0.797 0.919 0.001 0.035 TPST1 0.754 SPARC 0.661 0.848 0.029 0.007 CD163 0.808 GALNT2 0.673 0.883 0.008 0.01 CD163 0.808 HDHD1A 0.7 0.87 0.017 0.018 CD163 0.808 FAM118B 0.74 0.883 0.003 0.048 FFAR2 0.744 MTHFS 0.733 0.891 0.005 0.01 FFAR2 0.744 CDA 0.694 0.829 0.034 0.026 FFAR2 0.744 KDM6B_TMEM88 0.672 0.852 0.009 0.017 PHOSPHO1 0.63 HIST1H3H 0.836 0.927 0 0.021 PHOSPHO1 0.63 POLE2 0.704 0.838 0.01 0.006 PPIF 0.879 GAB2 0.842 0.937 0.031 0.036 PPIF 0.879 KIAA1257_ACAD9/ 0.823 0.943 0.048 0.009 PPIF 0.879 MPZL3 0.889 0.965 0.006 0.05 MTHFS 0.733 MTRR 0.832 0.922 0.002 0.033 MTHFS 0.733 AIG1 0.873 0.958 0 0.03 MTHFS 0.733 PCOLCE2 0.667 0.873 0.005 0.002 MTHFS 0.733 HS2ST1_UBA2 0.837 0.939 0 0.032 MTHFS 0.733 GALNT2 0.673 0.901 0.001 0.001 MTHFS 0.733 GLT25D1 0.808 0.904 0.002 0.041 MTHFS 0.733 HDHD1A 0.7 0.855 0.022 0.006 MTHFS 0.733 DPH3 0.706 0.855 0.047 0.01 MTHFS 0.733 DLEU2_DLEU2L 0.758 0.848 0.029 0.049 MTHFS 0.733 IL1B 0.769 0.874 0.044 0.007 MTHFS 0.733 LAIR1_LAIR2 0.777 0.88 0.013 0.018 MTHFS 0.733 NA 0.87 0.933 0.001 0.022 MTHFS 0.733 FSD1L 0.834 0.906 0.003 0.026 MTHFS 0.733 DYNLL1 0.787 0.886 0.011 0.01 MTHFS 0.733 FAM118B 0.74 0.861 0.014 0.026 MTHFS 0.733 PLEKHF2 0.737 0.86 0.04 0.027 MTHFS 0.733 AIF1 0.745 0.865 0.029 0.028 MTHFS 0.733 SDHC 0.85 0.927 0.006 0.01 DNAJC9_FAM149B1 0.742 PYHIN1 0.647 0.851 0.008 0.01 DNAJC9_FAM149B1 0.742 PPP1R2_PPP1R2P3 0.637 0.811 0.026 0.033 LGALS2 0.77 SULF2 0.775 0.911 0.001 0.029 LGALS2 0.77 RASA4_RASA4P_R 0.713 0.872 0.034 0.005 LGALS2 0.77 RBP7 0.803 0.902 0.005 0.024 LGALS2 0.77 CAMK1D 0.735 0.873 0.011 0.009 LGALS2 0.77 MPZL2 0.775 0.877 0.025 0.034 SIAE 0.894 GAB2 0.842 0.945 0.04 0.027 SIAE 0.894 IRS2 0.851 0.958 0.032 0.012 SIAE 0.894 OCR1 0.934 0.975 0.036 0.026 SIAE 0.894 NA 0.884 0.967 0.033 0.008 SIAE 0.894 HAL 0.846 0.958 0.037 0.006 SIAE 0.894 CCDC125 0.79 0.969 0.026 0.001 SIAE 0.894 SYNE2 0.886 0.965 0.031 0.013 SIAE 0.894 RGS2 0.789 0.953 0.03 0.002 ABCA13 0.699 CDA 0.694 0.857 0.002 0.04 ABCA13 0.699 DAAM2_LOC100131 0.778 0.866 0.029 0.002 ABCA13 0.699 ACPL2 0.756 0.873 0.006 0.017 ABCA13 0.699 THBS1 0.698 0.832 0.032 0.01 EFCAB2 0.784 KDM6B_TMEM88 0.672 0.845 0.016 0.03 EFCAB2 0.784 CAMK1D 0.735 0.863 0.011 0.04 EFCAB2 0.784 HIST2H2BF_HIST2 0.62 0.856 0.048 0.003 HIST1H2AA 0.825 AIG1 0.873 0.93 0.013 0.049 HIST1H2AA 0.825 PCOLCE2 0.667 0.905 0.033 0.001 HIST1H2AA 0.825 TYMS 0.838 0.912 0.026 0.044 HIST1H2AA 0.825 HIST1H2BM 0.862 0.925 0.019 0.028 HIST1H2AA 0.825 NA 0.87 0.926 0.037 0.037 HIST1H2AA 0.825 TNFRSF17 0.827 0.928 0.038 0.004 HINT1 0.819 NA 0.884 0.966 0.001 0.038 HINT1 0.819 LY6G5B_CSNK2B 0.63 0.879 0.01 0.002 HINT1 0.819 CCDC125 0.79 0.915 0.002 0.043 HINT1 0.819 ZRANB1 0.718 0.891 0.031 0.005 HINT1 0.819 PYHIN1 0.647 0.913 0.015 0 HINT1 0.819 CYP4F3_CYP4F2 0.755 0.897 0.044 0.009 HINT1 0.819 MME 0.847 0.93 0.008 0.05 HINT1 0.819 RASA4_RASA4P_R 0.713 0.879 0.039 0.014 HINT1 0.819 RBP7 0.803 0.919 0.005 0.042 HINT1 0.819 CAMK1D 0.735 0.887 0.004 0.029 HINT1 0.819 PPP1R2_PPP1R2P3 0.637 0.889 0.024 0.001 HIST1H3J 0.829 CDA 0.694 0.888 0.038 0.004 HIST1H3J 0.829 SAP30 0.789 0.932 0.003 0.015 HIST1H3J 0.829 AREG 0.814 0.917 0.009 0.047 HIST1H3J 0.829 NA 0.884 0.963 0.005 0.013 HIST1H3J 0.829 CCDC125 0.79 0.942 0.001 0.015 HIST1H3J 0.829 ZRANB1 0.718 0.922 0.033 0 HIST1H3J 0.829 CYP4F3_CYP4F2 0.755 0.925 0.034 0.002 HIST1H3J 0.829 MME 0.847 0.949 0.012 0.013 HIST1H3J 0.829 RBP7 0.803 0.952 0.006 0.004 CDA 0.694 SUCNR1 0.861 0.912 0.001 0.047 CDA 0.694 AIG1 0.873 0.961 0 0.014 CDA 0.694 PCOLCE2 0.667 0.857 0.004 0.006 CDA 0.694 HIST1H3A 0.822 0.908 0.001 0.018 CDA 0.694 GALNT2 0.673 0.832 0.015 0.021 CDA 0.694 HDHD1A 0.7 0.819 0.044 0.024 CDA 0.694 DPH3 0.706 0.85 0.027 0.004 CDA 0.694 ITGA2B 0.752 0.827 0.036 0.048 CDA 0.694 HIST1H3B 0.829 0.893 0.005 0.038 CDA 0.694 UBE2F_C20orf194 0.758 0.874 0.016 0.005 CDA 0.694 HIST1H3C 0.854 0.9 0.004 0.027 CDA 0.694 CD151 0.767 0.875 0.003 0.034 CDA 0.694 FSD1L 0.834 0.894 0.002 0.043 CDA 0.694 TPX2 0.745 0.862 0.011 0.011 CDA 0.694 IGK@_IGKC_IGKV 0.774 0.848 0.045 0.016 CDA 0.694 HIST1H2BJ 0.797 0.889 0.007 0.015 CDA 0.694 DYNLL1 0.787 0.858 0.017 0.039 CDA 0.694 PLEKHF2 0.737 0.851 0.02 0.042 CDA 0.694 AIF1 0.745 0.837 0.018 0.036 CDA 0.694 IGLV6-57 0.757 0.843 0.035 0.012 CDA 0.694 SDHC 0.85 0.926 0.003 0.005 SAP30 0.789 SUCNR1 0.861 0.923 0.012 0.02 SAP30 0.789 HS2ST1_UBA2 0.837 0.913 0.021 0.039 SAP30 0.789 HIST1H3A 0.822 0.923 0.013 0.004 SAP30 0.789 ACTA2 0.818 0.913 0.014 0.019 SAP30 0.789 EIF1AX_SCARNA9L 0.814 0.891 0.048 0.008 SAP30 0.789 TYMS 0.838 0.932 0.002 0.028 SAP30 0.789 HIST1H3B 0.829 0.906 0.043 0.011 SAP30 0.789 FSD1L_GARNL1 0.844 0.939 0.004 0.014 SAP30 0.789 PTGS1 0.794 0.911 0.023 0.004 SAP30 0.789 HIST1H3C 0.854 0.937 0.019 0.001 SAP30 0.789 HIST1H3H 0.836 0.931 0.02 0.003 SAP30 0.789 FSD1L 0.834 0.919 0.016 0.013 SAP30 0.789 TPX2 0.745 0.881 0.046 0.013 SAP30 0.789 APOLD1 0.777 0.89 0.038 0.016 SAP30 0.789 HIST1H2BJ 0.797 0.914 0.038 0.003 SAP30 0.789 LASS4 0.669 0.878 0.012 0.003 SAP30 0.789 B4GALT3 0.785 0.89 0.033 0.024 SAP30 0.789 TNFRSF17 0.827 0.902 0.018 0.014 SAP30 0.789 PLEKHA3 0.777 0.876 0.049 0.037 SAP30 0.789 CMTM5 0.814 0.935 0.012 0.002 SAP30 0.789 IGL@_IGLV1-44_(—) 0.77 0.883 0.021 0.019 SAP30 0.789 GLDC 0.775 0.921 0.003 0.007 SAP30 0.789 KIAA0101_CSNK1G 0.87 0.929 0.009 0.03 AGTRAP 0.928 MPZL3 0.889 0.991 0.034 0.003 SUCNR1 0.861 SLC11A1 0.705 0.905 0.031 0.003 SUCNR1 0.861 AREG 0.814 0.935 0.01 0.019 SUCNR1 0.861 PDE3B 0.861 0.948 0.011 0.037 SUCNR1 0.861 ERGIC1 0.758 0.913 0.027 0.007 SUCNR1 0.861 NA 0.884 0.965 0.004 0.028 SUCNR1 0.861 HAL 0.846 0.94 0.004 0.044 SUCNR1 0.861 KIAA1257_ACAD9/ 0.823 0.939 0.007 0.028 SUCNR1 0.861 CCDC125 0.79 0.953 0.004 0.006 SUCNR1 0.861 ZRANB1 0.718 0.939 0.008 0.001 SUCNR1 0.861 CYP4F3_CYP4F2 0.755 0.95 0.005 0.001 SUCNR1 0.861 MME 0.847 0.965 0.005 0.007 SUCNR1 0.861 RBP7 0.803 0.954 0.014 0.003 SUCNR1 0.861 RGS2 0.789 0.925 0.006 0.036 MTRR 0.832 AREG 0.814 0.896 0.047 0.034 MTRR 0.832 NA 0.884 0.954 0.007 0.019 MTRR 0.832 CCDC125 0.79 0.929 0.012 0.01 MTRR 0.832 RGS2 0.789 0.907 0.024 0.033 PLA2G7 0.738 MACF1 0.699 0.826 0.043 0.021 PLA2G7 0.738 HSP90AB1_HSP90A 0.65 0.84 0.027 0.008 PLA2G7 0.738 ITGA4_CERKL 0.721 0.854 0.008 0.035 PLA2G7 0.738 IL1B 0.769 0.888 0.002 0.042 PLA2G7 0.738 C7orf58 0.787 0.937 0 0.004 PLA2G7 0.738 DYNLL1 0.787 0.901 0.001 0.037 AIG1 0.873 GAB2 0.842 0.949 0.019 0.011 AIG1 0.873 AREG 0.814 0.956 0.015 0.003 AIG1 0.873 NA 0.884 0.964 0.013 0.011 AIG1 0.873 KIAA1257_ACAD9/ 0.823 0.952 0.041 0.002 AIG1 0.873 CCDC125 0.79 0.965 0.016 0.002 AIG1 0.873 SYNE2 0.886 0.954 0.017 0.039 AIG1 0.873 RGS2 0.789 0.946 0.018 0.003 PCOLCE2 0.667 SLC11A1 0.705 0.845 0.016 0.003 PCOLCE2 0.667 AREG 0.814 0.907 0 0.016 PCOLCE2 0.667 DAAM2_LOC100131 0.778 0.848 0.023 0.017 PCOLCE2 0.667 ACPL2 0.756 0.848 0.02 0.002 PCOLCE2 0.667 ERGIC1 0.758 0.831 0.019 0.031 PCOLCE2 0.667 CYP4F3_CYP4F2 0.755 0.823 0.04 0.048 GAB2 0.842 GSTO1 0.83 0.915 0.05 0.017 GAB2 0.842 PMS2CL_PMS2 0.857 0.926 0.043 0.027 GAB2 0.842 NEK6_LOC1001290 0.844 0.927 0.035 0.015 GAB2 0.842 ACTA2 0.818 0.965 0.012 0.001 GAB2 0.842 CD300A 0.888 0.974 0.004 0.01 GAB2 0.842 TRIM21 0.857 0.972 0.006 0.008 GAB2 0.842 ICAM1 0.895 0.964 0.008 0.02 GAB2 0.842 P4HA1_RPL17 0.88 0.945 0.01 0.044 GAB2 0.842 RRP12_LOC644215 0.84 0.958 0.009 0.004 GAB2 0.842 TIMM10 0.868 0.933 0.049 0.017 GAB2 0.842 IRF1 0.894 0.979 0.001 0.015 GAB2 0.842 ATP6V0D1_LOC100 0.828 0.954 0.002 0.023 GAB2 0.842 CDC26 0.868 0.941 0.012 0.021 HS2ST1_UBA2 0.837 PDE3B 0.861 0.943 0.012 0.032 HS2ST1_UBA2 0.837 NA 0.884 0.943 0.024 0.026 HS2ST1_UBA2 0.837 HAL 0.846 0.949 0.013 0.01 HS2ST1_UBA2 0.837 KIAA1257_ACAD9/ 0.823 0.926 0.037 0.012 HS2ST1_UBA2 0.837 SYNE2 0.886 0.953 0.009 0.024 HS2ST1_UBA2 0.837 RGS2 0.789 0.967 0.003 0.001 HIST1H3A 0.822 SLC11A1 0.705 0.897 0.028 0.007 HIST1H3A 0.822 AREG 0.814 0.927 0.003 0.031 HIST1H3A 0.822 ERGIC1 0.758 0.917 0.01 0.008 HIST1H3A 0.822 NA 0.884 0.96 0.002 0.016 HIST1H3A 0.822 KIAA1257_ACAD9/ 0.823 0.937 0.002 0.027 HIST1H3A 0.822 CCDC125 0.79 0.945 0.002 0.01 HIST1H3A 0.822 CYP4F3_CYP4F2 0.755 0.91 0.028 0.004 HIST1H3A 0.822 MME 0.847 0.945 0.006 0.018 HIST1H3A 0.822 RBP7 0.803 0.939 0.019 0.002 HIST1H3A 0.822 CAMK1D 0.735 0.887 0.023 0.015 SLC39A8 0.867 NA 0.884 0.951 0.019 0.045 SLC39A8 0.867 CCDC125 0.79 0.946 0.019 0.006 SLC39A8 0.867 RGS2 0.789 0.922 0.021 0.034 MKI67 0.674 CCDC125 0.79 0.925 0 0.034 MKI67 0.674 CYP4F3_CYP4F2 0.755 0.855 0.009 0.028 MKI67 0.674 RBP7 0.803 0.898 0.001 0.027 SLC11A1 0.705 GALNT2 0.673 0.831 0.009 0.022 SLC11A1 0.705 GLT25D1 0.808 0.902 0.001 0.038 SLC11A1 0.705 DPH3 0.706 0.85 0.028 0.002 SLC11A1 0.705 DLEU2_DLEU2L 0.758 0.845 0.044 0.024 SLC11A1 0.705 ITGA2B 0.752 0.84 0.05 0.012 SLC11A1 0.705 HIST1H3B 0.829 0.889 0.011 0.049 SLC11A1 0.705 PTGS1 0.794 0.875 0.007 0.033 SLC11A1 0.705 CD151 0.767 0.874 0.007 0.019 SLC11A1 0.705 APOLD1 0.777 0.874 0.009 0.039 SLC11A1 0.705 HIST1H2BJ 0.797 0.88 0.017 0.019 SLC11A1 0.705 LASS4 0.669 0.822 0.032 0.019 SLC11A1 0.705 PLEKHF2 0.737 0.851 0.025 0.013 SLC11A1 0.705 CMTM5 0.814 0.892 0.007 0.025 SLC11A1 0.705 AIF1 0.745 0.841 0.027 0.026 SLC11A1 0.705 KIAA0101_CSNK1G 0.87 0.906 0.005 0.048 AREG 0.814 GSTO1 0.83 0.918 0.018 0.011 AREG 0.814 PTGER2 0.842 0.923 0.007 0.029 AREG 0.814 RPL17_SNORD58B 0.842 0.909 0.02 0.045 AREG 0.814 GLT25D1 0.808 0.92 0.011 0.012 AREG 0.814 ACTA2 0.818 0.905 0.049 0.021 AREG 0.814 EIF1AX_SCARNA9L 0.814 0.905 0.049 0.007 AREG 0.814 TAF13 0.908 0.958 0.005 0.036 AREG 0.814 TYMS 0.838 0.92 0.022 0.019 AREG 0.814 HIST1H2BM 0.862 0.939 0.009 0.019 AREG 0.814 LAIR1_LAIR2 0.777 0.901 0.036 0.017 AREG 0.814 HIST1H3B 0.829 0.926 0.048 0.004 AREG 0.814 FSD1L_GARNL1 0.844 0.942 0.006 0.011 AREG 0.814 NA 0.87 0.932 0.02 0.029 AREG 0.814 HIST1H3C 0.854 0.923 0.038 0.011 AREG 0.814 FSD1L 0.834 0.933 0.022 0.004 AREG 0.814 TNFRSF17 0.827 0.914 0.015 0.02 AREG 0.814 PLEKHA3 0.777 0.904 0.03 0.005 AREG 0.814 CDC26 0.868 0.944 0.005 0.035 AREG 0.814 GLDC 0.775 0.919 0.018 0.004 AREG 0.814 KIAA0101_CSNK1G 0.87 0.927 0.031 0.03 DAAM2_LOC100131 0.778 DPH3 0.706 0.858 0.007 0.037 DAAM2_LOC100131 0.778 ANKRD28 0.71 0.859 0.014 0.032 DAAM2_LOC100131 0.778 MYL9 0.695 0.842 0.041 0.03 LTF 0.642 THBS1 0.698 0.823 0.002 0.031 TREML1 0.704 NF-E4 0.674 0.864 0.023 0.002 TREML1 0.704 THBS1 0.698 0.858 0.008 0.008 TREML1 0.704 MME 0.847 0.94 0 0.031 GSTO1 0.83 PDE3B 0.861 0.929 0.025 0.05 GSTO1 0.83 NA 0.884 0.972 0.004 0.008 GSTO1 0.83 KIAA1257_ACAD9/ 0.823 0.923 0.026 0.012 GSTO1 0.83 CCDC125 0.79 0.948 0.012 0.003 GSTO1 0.83 CYP4F3_CYP4F2 0.755 0.912 0.049 0.004 GSTO1 0.83 SYNE2 0.886 0.953 0.008 0.028 GSTO1 0.83 RGS2 0.789 0.926 0.01 0.013 PTGER2 0.842 PDE3B 0.861 0.953 0.018 0.009 PTGER2 0.842 IRS2 0.851 0.932 0.035 0.019 PTGER2 0.842 NA 0.884 0.957 0.021 0.008 PTGER2 0.842 HAL 0.846 0.934 0.048 0.005 PTGER2 0.842 CCDC125 0.79 0.937 0.042 0.005 PTGER2 0.842 SYNE2 0.886 0.971 0.004 0.02 PTGER2 0.842 RGS2 0.789 0.952 0.015 0.001 PMS2CL_PMS2 0.857 PDE3B 0.861 0.961 0.009 0.015 PMS2CL_PMS2 0.857 NA 0.884 0.977 0.005 0.01 PMS2CL_PMS2 0.857 HAL 0.846 0.938 0.02 0.029 PMS2CL_PMS2 0.857 KIAA1257_ACAD9/ 0.823 0.942 0.018 0.011 PMS2CL_PMS2 0.857 CCDC125 0.79 0.96 0.012 0.002 PMS2CL_PMS2 0.857 SYNE2 0.886 0.993 0.002 0.006 PMS2CL_PMS2 0.857 RBP7 0.803 0.952 0.022 0.002 PMS2CL_PMS2 0.857 RGS2 0.789 0.93 0.017 0.011 PMS2CL_PMS2 0.857 CAMK1D 0.735 0.92 0.02 0.002 PDE3B 0.861 NEK6_LOC1001290 0.844 0.95 0.004 0.035 PDE3B 0.861 AMFR 0.874 0.961 0.01 0.039 PDE3B 0.861 RPL17_SNORD58B 0.842 0.961 0.05 0.001 PDE3B 0.861 GLT25D1 0.808 0.916 0.034 0.031 PDE3B 0.861 EIF1AX_SCARNA9L 0.814 0.956 0.039 0 PDE3B 0.861 ZNF28 0.891 0.973 0.003 0.05 PDE3B 0.861 TYMS 0.838 0.939 0.034 0.038 PDE3B 0.861 HIST1H2BM 0.862 0.945 0.032 0.034 PDE3B 0.861 FSD1L_GARNL1 0.844 0.959 0.009 0.019 PDE3B 0.861 PTGS1 0.794 0.943 0.038 0.001 PDE3B 0.861 FSD1L 0.834 0.948 0.015 0.005 PDE3B 0.861 APOLD1 0.777 0.95 0.023 0.001 PDE3B 0.861 SON 0.794 0.946 0.012 0.007 PDE3B 0.861 PLEKHA3 0.777 0.946 0.018 0.001 PDE3B 0.861 CDC26 0.868 0.958 0.006 0.039 PDE3B 0.861 GLDC 0.775 0.922 0.028 0.008 PDE3B 0.861 KIAA0101_CSNK1G 0.87 0.953 0.05 0.008 SULF2 0.775 IRF4 0.74 0.868 0.015 0.046 SULF2 0.775 RPL17_SNORD58B 0.842 0.904 0.017 0.034 SULF2 0.775 ACTA2 0.818 0.924 0.003 0.033 SULF2 0.775 PTGS1 0.794 0.909 0.009 0.015 SULF2 0.775 E2F6 0.798 0.906 0.018 0.01 SULF2 0.775 CD151 0.767 0.896 0.02 0.01 SULF2 0.775 TPX2 0.745 0.874 0.017 0.026 SULF2 0.775 C7orf58 0.787 0.9 0.028 0.002 SULF2 0.775 DYNLL1 0.787 0.893 0.03 0.013 SULF2 0.775 NP 0.787 0.895 0.015 0.024 SULF2 0.775 NFXL1 0.808 0.889 0.044 0.044 SULF2 0.775 DCTN5 0.773 0.892 0.01 0.042 NEK6_LOC1001290 0.844 NA 0.884 0.931 0.045 0.038 NEK6_LOC1001290 0.844 SYNE2 0.886 0.946 0.027 0.026 NEK6_LOC1001290 0.844 RGS2 0.789 0.936 0.038 0.001 CENPK 0.739 PYHIN1 0.647 0.854 0.026 0.007 CENPK 0.739 PPP1R2_PPP1R2P3 0.637 0.812 0.032 0.043 TRAF3 0.889 SYNE2 0.886 0.962 0.032 0.017 IRF4 0.74 NA 0.884 0.935 0.002 0.034 IRF4 0.74 CCDC125 0.79 0.89 0.003 0.044 IRF4 0.74 CYP4F3_CYP4F2 0.755 0.874 0.04 0.004 IRF4 0.74 SYNE2 0.886 0.954 0 0.017 IRF4 0.74 MME 0.847 0.906 0.008 0.022 IRF4 0.74 RBP7 0.803 0.873 0.032 0.04 MACF1 0.699 ACPL2 0.756 0.858 0.008 0.041 MACF1 0.699 CAMK1D 0.735 0.854 0.003 0.05 MACF1 0.699 PPP1R2_PPP1R2P3 0.637 0.797 0.026 0.034 AMFR 0.874 IRS2 0.851 0.952 0.031 0.011 AMFR 0.874 OCR1 0.934 0.977 0.033 0.021 AMFR 0.874 NA 0.884 0.965 0.042 0.005 AMFR 0.874 HAL 0.846 0.971 0.028 0.003 AMFR 0.874 ZRANB1 0.718 0.965 0.044 0 AMFR 0.874 SYNE2 0.886 0.973 0.023 0.01 AMFR 0.874 RGS2 0.789 0.954 0.019 0.004 RPL17_SNORD58B 0.842 NA 0.884 0.98 0.004 0.006 RPL17_SNORD58B 0.842 LY6G5B_CSNK2B 0.63 0.892 0.044 0 RPL17_SNORD58B 0.842 KIAA1257_ACAD9/ 0.823 0.922 0.011 0.049 RPL17_SNORD58B 0.842 CCDC125 0.79 0.939 0.005 0.015 RPL17_SNORD58B 0.842 ZRANB1 0.718 0.921 0.039 0.001 RPL17_SNORD58B 0.842 MME 0.847 0.947 0.011 0.017 RPL17_SNORD58B 0.842 RBP7 0.803 0.944 0.005 0.011 RPL17_SNORD58B 0.842 CAMK1D 0.735 0.894 0.023 0.014 IRS2 0.851 GLT25D1 0.808 0.926 0.023 0.016 IRS2 0.851 RRP12_LOC644215 0.84 0.938 0.008 0.038 IRS2 0.851 PTGS1 0.794 0.953 0.021 0.001 IRS2 0.851 CMTM5 0.814 0.949 0.031 0.002 IRS2 0.851 GLDC 0.775 0.946 0.016 0.001 GALNT2 0.673 ACPL2 0.756 0.832 0.033 0.019 GALNT2 0.673 ERGIC1 0.758 0.845 0.011 0.018 GLT25D1 0.808 ERGIC1 0.758 0.905 0.046 0.001 GLT25D1 0.808 HAL 0.846 0.916 0.024 0.019 GLT25D1 0.808 RGS2 0.789 0.887 0.038 0.016 GLT25D1 0.808 CAMK1D 0.735 0.925 0.023 0 HDHD1A 0.7 ACPL2 0.756 0.855 0.01 0.018 HDHD1A 0.7 LY6G5B_CSNK2B 0.63 0.811 0.028 0.009 HDHD1A 0.7 CCDC125 0.79 0.927 0 0.021 HDHD1A 0.7 ZRANB1 0.718 0.84 0.018 0.02 HDHD1A 0.7 CYP4F3_CYP4F2 0.755 0.844 0.028 0.018 HDHD1A 0.7 SYNE2 0.886 0.952 0 0.047 HDHD1A 0.7 CPM 0.812 0.906 0.002 0.009 HDHD1A 0.7 CAMK1D 0.735 0.871 0.001 0.021 ACTA2 0.818 ERGIC1 0.758 0.906 0.03 0.008 ACTA2 0.818 HAL 0.846 0.944 0.006 0.028 ACTA2 0.818 KIAA1257_ACAD9/ 0.823 0.932 0.006 0.018 ACTA2 0.818 CCDC125 0.79 0.914 0.03 0.012 ACTA2 0.818 ZRANB1 0.718 0.93 0.021 0 ACTA2 0.818 MPZL2 0.775 0.919 0.034 0.004 ACPL2 0.756 DPH3 0.706 0.882 0.009 0.008 ACPL2 0.756 ANKRD28 0.71 0.906 0.005 0.001 ACPL2 0.756 ITGA2B 0.752 0.878 0.009 0.016 ACPL2 0.756 HIST1H3C 0.854 0.935 0.002 0.016 ACPL2 0.756 MYL9 0.695 0.861 0.022 0.006 ACPL2 0.756 HIST1H2BJ 0.797 0.92 0.004 0.01 ACPL2 0.756 MPO 0.684 0.832 0.025 0.043 ACPL2 0.756 FAM118B 0.74 0.869 0.008 0.033 ACPL2 0.756 SDHC 0.85 0.93 0.002 0.038 OCR1 0.934 ZNF28 0.891 0.976 0.038 0.043 OCR1 0.934 HSPB1_HSPBL2 0.886 0.977 0.018 0.033 OCR1 0.934 PTGS1 0.794 0.974 0.023 0.001 OCR1 0.934 MYL9 0.695 0.977 0.028 0 OCR1 0.934 CMTM5 0.814 0.979 0.024 0.002 OCR1 0.934 CDC26 0.868 0.965 0.044 0.033 EIF1AX_SCARNA9L 0.814 LY6G5B_CSNK2B 0.63 0.88 0.024 0.001 EIF1AX_SCARNA9L 0.814 CCDC125 0.79 0.929 0.001 0.028 EIF1AX_SCARNA9L 0.814 ZRANB1 0.718 0.901 0.009 0.004 EIF1AX_SCARNA9L 0.814 MME 0.847 0.94 0.008 0.012 EIF1AX_SCARNA9L 0.814 CPM 0.812 0.9 0.029 0.048 EIF1AX_SCARNA9L 0.814 RASA4_RASA4P_R 0.713 0.881 0.041 0.008 EIF1AX_SCARNA9L 0.814 RBP7 0.803 0.937 0.005 0.013 EIF1AX_SCARNA9L 0.814 CAMK1D 0.735 0.898 0.003 0.013 DPH3 0.706 ERGIC1 0.758 0.883 0 0.035 DPH3 0.706 HAL 0.846 0.938 0 0.05 DPH3 0.706 RBP7 0.803 0.905 0.001 0.048 ERGIC1 0.758 HIST1H3B 0.829 0.89 0.031 0.035 ERGIC1 0.758 PTGS1 0.794 0.91 0.008 0.004 ERGIC1 0.758 HIST1H3C 0.854 0.901 0.03 0.013 ERGIC1 0.758 CD151 0.767 0.885 0.018 0.014 ERGIC1 0.758 HIST1H3H 0.836 0.898 0.032 0.019 ERGIC1 0.758 FSD1L 0.834 0.901 0.005 0.042 ERGIC1 0.758 TPX2 0.745 0.861 0.037 0.023 ERGIC1 0.758 APOLD1 0.777 0.917 0.004 0.004 ERGIC1 0.758 HIST1H2BJ 0.797 0.901 0.032 0.003 ERGIC1 0.758 LASS4 0.669 0.85 0.017 0.012 ERGIC1 0.758 PLEKHF2 0.737 0.867 0.043 0.011 ERGIC1 0.758 CMTM5 0.814 0.927 0.005 0.006 ERGIC1 0.758 AIF1 0.745 0.87 0.028 0.007 ERGIC1 0.758 CDC26 0.868 0.955 0 0.05 ERGIC1 0.758 SDHC 0.85 0.919 0.006 0.015 ERGIC1 0.758 GLDC 0.775 0.893 0.004 0.023 CD300A 0.888 MPZL3 0.889 0.974 0.014 0.01 NF-E4 0.674 MYL9 0.695 0.868 0 0.023 NF-E4 0.674 HIST1H2BJ 0.797 0.926 0 0.027 NF-E4 0.674 SPARC 0.661 0.834 0.007 0.02 MINPP1 0.718 ZRANB1 0.718 0.895 0 0.009 MINPP1 0.718 RPIA 0.655 0.887 0.011 0 MINPP1 0.718 C1orf128 0.662 0.86 0.033 0.001 TRIM21 0.857 NA 0.884 0.945 0.029 0.048 TRIM21 0.857 SYNE2 0.886 0.956 0.024 0.033 ZNF28 0.891 NA 0.884 0.977 0.019 0.008 ZNF28 0.891 SYNE2 0.886 0.982 0.012 0.008 ZNF28 0.891 RGS2 0.789 0.953 0.044 0.004 NA 0.884 TAF13 0.908 0.984 0.005 0.026 NA 0.884 P4HA1_RPL17 0.88 0.969 0.018 0.018 NA 0.884 TYMS 0.838 0.953 0.018 0.012 NA 0.884 RRP12_LOC644215 0.84 0.939 0.019 0.048 NA 0.884 HIST1H2BM 0.862 0.977 0.006 0.007 NA 0.884 HIST1H3B 0.829 0.961 0.034 0.003 NA 0.884 TIMM10 0.868 0.958 0.01 0.034 NA 0.884 FSD1L_GARNL1 0.844 0.951 0.02 0.024 NA 0.884 PTGS1 0.794 0.949 0.033 0.002 NA 0.884 NA 0.87 0.963 0.025 0.004 NA 0.884 HIST1H3C 0.854 0.971 0.023 0.003 NA 0.884 HIST1H3H 0.836 0.972 0.018 0.002 NA 0.884 FSD1L 0.834 0.956 0.032 0.003 NA 0.884 TPX2 0.745 0.955 0.015 0.001 NA 0.884 APOLD1 0.777 0.943 0.013 0.004 NA 0.884 MPO 0.684 0.94 0.04 0 NA 0.884 B4GALT3 0.785 0.948 0.03 0.002 NA 0.884 CTSL1_CTSLL3 0.819 0.945 0.033 0.009 NA 0.884 TNFRSF17 0.827 0.962 0.029 0.002 NA 0.884 PLEKHA3 0.777 0.951 0.033 0.001 NA 0.884 CMTM5 0.814 0.944 0.032 0.009 NA 0.884 IGL@_IGLV1-44_(—) 0.77 0.939 0.028 0.002 NA 0.884 DCTN5 0.773 0.939 0.034 0.003 NA 0.884 KIAA0101_CSNK1G 0.87 0.985 0.006 0.008 TAF13 0.908 CCDC125 0.79 0.965 0.031 0.003 TAF13 0.908 SYNE2 0.886 0.965 0.036 0.043 TAF13 0.908 RGS2 0.789 0.953 0.048 0.008 P4HA1_RPL17 0.88 CCDC125 0.79 0.961 0.032 0.002 P4HA1_RPL17 0.88 SYNE2 0.886 0.984 0.005 0.016 C15orf54 0.672 POLE2 0.704 0.824 0.039 0.01 KLHL5 0.913 SYNE2 0.886 0.976 0.03 0.016 KLHL5 0.913 RGS2 0.789 0.963 0.011 0.005 HAL 0.846 TYMS 0.838 0.932 0.02 0.031 HAL 0.846 RRP12_LOC644215 0.84 0.936 0.011 0.031 HAL 0.846 TIMM10 0.868 0.931 0.034 0.041 HAL 0.846 FSD1L_GARNL1 0.844 0.937 0.015 0.046 HAL 0.846 PTGS1 0.794 0.955 0.016 0.001 HAL 0.846 CD151 0.767 0.926 0.047 0.002 HAL 0.846 FSD1L 0.834 0.919 0.041 0.019 HAL 0.846 APOLD1 0.777 0.962 0.006 0.001 HAL 0.846 SON 0.794 0.918 0.043 0.011 HAL 0.846 LASS4 0.669 0.925 0.015 0 HAL 0.846 B4GALT3 0.785 0.945 0.015 0.002 HAL 0.846 PLEKHA3 0.777 0.912 0.045 0.01 HAL 0.846 CMTM5 0.814 0.97 0.013 0.001 HAL 0.846 AIF1 0.745 0.936 0.018 0.001 HAL 0.846 CDC26 0.868 0.957 0.004 0.036 HAL 0.846 GLDC 0.775 0.946 0.01 0.002 HAL 0.846 DCTN5 0.773 0.905 0.023 0.012 DLEU2_DLEU2L 0.758 KIAA1257_ACAD9/ 0.823 0.918 0.002 0.033 DLEU2_DLEU2L 0.758 MME 0.847 0.916 0.004 0.045 DLEU2_DLEU2L 0.758 CAMK1D 0.735 0.87 0.008 0.022 ANKRD28 0.71 LY6G5B_CSNK2B 0.63 0.791 0.024 0.048 ANKRD28 0.71 THBS1 0.698 0.836 0.039 0.005 ANKRD28 0.71 ZRANB1 0.718 0.874 0.001 0.016 ANKRD28 0.71 CPM 0.812 0.9 0.002 0.032 ANKRD28 0.71 CAMK1D 0.735 0.867 0.001 0.036 LY6G5B_CSNK2B 0.63 KIAA0746 0.713 0.792 0.048 0.015 KIAA1257_ACAD9/ 0.823 TYMS 0.838 0.929 0.012 0.039 KIAA1257_ACAD9/ 0.823 RRP12_LOC644215 0.84 0.939 0.004 0.038 KIAA1257_ACAD9/ 0.823 TIMM10 0.868 0.937 0.019 0.023 KIAA1257_ACAD9/ 0.823 PTGS1 0.794 0.935 0.016 0.002 KIAA1257_ACAD9/ 0.823 NA 0.87 0.928 0.018 0.036 KIAA1257_ACAD9/ 0.823 CD151 0.767 0.909 0.05 0.004 KIAA1257_ACAD9/ 0.823 HIST1H3H 0.836 0.933 0.045 0.002 KIAA1257_ACAD9/ 0.823 FSD1L 0.834 0.911 0.036 0.035 KIAA1257_ACAD9/ 0.823 APOLD1 0.777 0.941 0.007 0.003 KIAA1257_ACAD9/ 0.823 B4GALT3 0.785 0.938 0.009 0.007 KIAA1257_ACAD9/ 0.823 CMTM5 0.814 0.952 0.008 0.002 KIAA1257_ACAD9/ 0.823 SDHC 0.85 0.926 0.039 0.011 KIAA1257_ACAD9/ 0.823 GLDC 0.775 0.904 0.033 0.014 KIAA1257_ACAD9/ 0.823 KIAA0101_CSNK1G 0.87 0.939 0.044 0.015 MGST3 0.897 MPZL3 0.889 0.983 0.005 0.022 MGST3 0.897 RGS2 0.789 0.948 0.036 0.005 HSPB1_HSPBL2 0.886 MPZL3 0.889 0.976 0.005 0.024 TYMS 0.838 CCDC125 0.79 0.956 0.007 0.003 TYMS 0.838 SYNE2 0.886 0.965 0.004 0.025 CCDC125 0.79 HIST1H2BM 0.862 0.965 0.002 0.009 CCDC125 0.79 HIST1H3B 0.829 0.921 0.03 0.014 CCDC125 0.79 FSD1L_GARNL1 0.844 0.964 0.001 0.013 CCDC125 0.79 PTGS1 0.794 0.915 0.009 0.016 CCDC125 0.79 UBE2F_C20orf194 0.758 0.91 0.048 0.001 CCDC125 0.79 HIST1H3C 0.854 0.943 0.021 0.001 CCDC125 0.79 CD151 0.767 0.889 0.05 0.016 CCDC125 0.79 HIST1H3H 0.836 0.931 0.02 0.007 CCDC125 0.79 FSD1L 0.834 0.935 0.009 0.005 CCDC125 0.79 TPX2 0.745 0.969 0.003 0 CCDC125 0.79 APOLD1 0.777 0.886 0.014 0.039 CCDC125 0.79 HIST1H2BJ 0.797 0.932 0.032 0 CCDC125 0.79 SON 0.794 0.913 0.017 0.02 CCDC125 0.79 LASS4 0.669 0.846 0.032 0.015 CCDC125 0.79 DYNLL1 0.787 0.917 0.041 0.001 CCDC125 0.79 FAM118B 0.74 0.921 0.033 0 CCDC125 0.79 B4GALT3 0.785 0.923 0.014 0.005 CCDC125 0.79 CTSL1_CTSLL3 0.819 0.897 0.048 0.049 CCDC125 0.79 NP 0.787 0.927 0.012 0.003 CCDC125 0.79 TNFRSF17 0.827 0.927 0.009 0.012 CCDC125 0.79 PLEKHA3 0.777 0.901 0.022 0.014 CCDC125 0.79 TMEM62_SPCS2_L 0.849 0.94 0.012 0.018 CCDC125 0.79 CMTM5 0.814 0.922 0.013 0.017 CCDC125 0.79 IGL@_IGLV1-44_(—) 0.77 0.899 0.021 0.02 CCDC125 0.79 SDHC 0.85 0.932 0.031 0.007 CCDC125 0.79 GLDC 0.775 0.899 0.01 0.026 CCDC125 0.79 DCTN5 0.773 0.91 0.008 0.02 CCDC125 0.79 KIAA0101_CSNK1G 0.87 0.956 0.01 0.005 HIST1H2BM 0.862 SYNE2 0.886 0.982 0.006 0.011 HIST1H2BM 0.862 MME 0.847 0.947 0.047 0.004 HIST1H2BM 0.862 RGS2 0.789 0.932 0.028 0.011 IL1B 0.769 KDM6B_TMEM88 0.672 0.842 0.014 0.031 THBS1 0.698 ITGA2B 0.752 0.919 0 0.007 THBS1 0.698 MYL9 0.695 0.858 0.001 0.021 THBS1 0.698 SPARC 0.661 0.835 0.016 0.007 ITGA2B 0.752 ZRANB1 0.718 0.906 0 0.006 ITGA2B 0.752 CYP4F3_CYP4F2 0.755 0.894 0.011 0.008 ITGA2B 0.752 MME 0.847 0.941 0.001 0.014 ITGA2B 0.752 RBP7 0.803 0.913 0.003 0.015 ITGA2B 0.752 MPZL2 0.775 0.885 0.007 0.019 LAIR1_LAIR2 0.777 MPZL2 0.775 0.883 0.044 0.01 HIST1H3B 0.829 CYP4F3_CYP4F2 0.755 0.909 0.038 0.008 ZRANB1 0.718 PTGS1 0.794 0.945 0 0.002 ZRANB1 0.718 UBE2F_C20orf194 0.758 0.87 0.009 0.016 ZRANB1 0.718 HIST1H3C 0.854 0.914 0.003 0.038 ZRANB1 0.718 E2F6 0.798 0.899 0.01 0.011 ZRANB1 0.718 CD151 0.767 0.881 0.005 0.014 ZRANB1 0.718 HIST1H3H 0.836 0.946 0 0.006 ZRANB1 0.718 TPX2 0.745 0.861 0.004 0.049 ZRANB1 0.718 MYL9 0.695 0.866 0.031 0.001 ZRANB1 0.718 HIST1H2BJ 0.797 0.929 0.002 0.001 ZRANB1 0.718 C7orf58 0.787 0.886 0.01 0.003 ZRANB1 0.718 DYNLL1 0.787 0.887 0.013 0.009 ZRANB1 0.718 FAM118B 0.74 0.833 0.041 0.024 ZRANB1 0.718 B4GALT3 0.785 0.926 0 0.018 ZRANB1 0.718 NP 0.787 0.913 0.001 0.014 ZRANB1 0.718 TNFRSF17 0.827 0.891 0.003 0.031 ZRANB1 0.718 CMTM5 0.814 0.946 0 0.005 ZRANB1 0.718 IGLV6-57 0.757 0.846 0.026 0.047 TIMM10 0.868 SYNE2 0.886 0.971 0.017 0.007 FSD1L_GARNL1 0.844 SYNE2 0.886 0.965 0.007 0.02 FSD1L_GARNL1 0.844 RGS2 0.789 0.94 0.036 0.002 PTGS1 0.794 CYP4F3_CYP4F2 0.755 0.902 0.049 0.001 PTGS1 0.794 MME 0.847 0.951 0.005 0.004 PTGS1 0.794 CPM 0.812 0.906 0.031 0.006 PTGS1 0.794 RBP7 0.803 0.952 0.003 0.001 PTGS1 0.794 RGS2 0.789 0.91 0.004 0.027 PTGS1 0.794 CAMK1D 0.735 0.874 0.041 0.014 PTGS1 0.794 MPZL2 0.775 0.901 0.036 0.002 UBE2F_C20orf194 0.758 CYP4F3_CYP4F2 0.755 0.861 0.05 0.026 UBE2F_C20orf194 0.758 RBP7 0.803 0.906 0.008 0.012 HIST1H3C 0.854 RBP7 0.803 0.949 0.007 0.008 HIST1H3C 0.854 CAMK1D 0.735 0.9 0.021 0.016 FAM118A 0.717 SYNE2 0.886 0.961 0 0.018 FAM118A 0.717 RBP7 0.803 0.894 0.009 0.035 E2F6 0.798 RASA4_RASA4P_R 0.713 0.894 0.008 0.013 E2F6 0.798 RBP7 0.803 0.93 0.002 0.023 E2F6 0.798 CAMK1D 0.735 0.883 0.01 0.023 MPZL3 0.889 IRF1 0.894 0.971 0.011 0.026 MPZL3 0.889 ATP6V0D1_LOC100 0.828 0.965 0.027 0.004 MPZL3 0.889 CDC26 0.868 0.972 0.015 0.004 CD151 0.767 KDM6B_TMEM88 0.672 0.856 0.024 0.008 CD151 0.767 CYP4F3_CYP4F2 0.755 0.894 0.015 0.005 CD151 0.767 RBP7 0.803 0.919 0.009 0.004 CD151 0.767 RGS2 0.789 0.909 0.002 0.042 CD151 0.767 MPZL2 0.775 0.87 0.026 0.02 HIST1H3H 0.836 CYP4F3_CYP4F2 0.755 0.948 0.01 0 HIST1H3H 0.836 MME 0.847 0.973 0.004 0.003 HIST1H3H 0.836 RBP7 0.803 0.958 0.006 0.002 HIST1H3H 0.836 RGS2 0.789 0.923 0.001 0.046 FSD1L 0.834 RGS2 0.789 0.9 0.022 0.02 FSD1L 0.834 CAMK1D 0.735 0.912 0.028 0.001 TPX2 0.745 CYP4F3_CYP4F2 0.755 0.893 0.02 0.003 TPX2 0.745 SYNE2 0.886 0.965 0 0.017 TPX2 0.745 MME 0.847 0.913 0.009 0.022 TPX2 0.745 RBP7 0.803 0.908 0.02 0.006 TPX2 0.745 MPZL2 0.775 0.85 0.04 0.034 PYHIN1 0.647 IGK@_IGKC_IGKV 0.774 0.855 0.006 0.032 APOLD1 0.777 SYNE2 0.886 0.945 0.001 0.048 APOLD1 0.777 MME 0.847 0.917 0.013 0.011 APOLD1 0.777 RGS2 0.789 0.918 0.004 0.01 APOLD1 0.777 CAMK1D 0.735 0.887 0.031 0.003 KDM6B_TMEM88 0.672 AIF1 0.745 0.833 0.017 0.039 MYL9 0.695 CYP4F3_CYP4F2 0.755 0.886 0.004 0.009 MYL9 0.695 MME 0.847 0.949 0 0.007 MYL9 0.695 RBP7 0.803 0.948 0 0.004 HIST1H2BJ 0.797 CYP4F3_CYP4F2 0.755 0.929 0.003 0.004 HIST1H2BJ 0.797 MME 0.847 0.96 0.001 0.012 HIST1H2BJ 0.797 RBP7 0.803 0.957 0.001 0.005 CYP4F3_CYP4F2 0.755 LASS4 0.669 0.837 0.007 0.043 CYP4F3_CYP4F2 0.755 DYNLL1 0.787 0.89 0.007 0.021 CYP4F3_CYP4F2 0.755 MPO 0.684 0.861 0.039 0.005 CYP4F3_CYP4F2 0.755 FAM118B 0.74 0.85 0.039 0.041 CYP4F3_CYP4F2 0.755 TNFRSF17 0.827 0.927 0.002 0.013 CYP4F3_CYP4F2 0.755 CMTM5 0.814 0.927 0.001 0.035 CYP4F3_CYP4F2 0.755 IGLV6-57 0.757 0.933 0.001 0.002 CYP4F3_CYP4F2 0.755 IGL@_IGLV1-44_(—) 0.77 0.903 0.001 0.024 SON 0.794 SYNE2 0.886 0.962 0 0.047 SON 0.794 LOC100128751 0.809 0.926 0.005 0.008 SYNE2 0.886 B4GALT3 0.785 0.96 0.018 0.002 SYNE2 0.886 NP 0.787 0.965 0.013 0.001 SYNE2 0.886 PLEKHA3 0.777 0.955 0.042 0.001 SYNE2 0.886 CDC26 0.868 0.967 0.008 0.013 SYNE2 0.886 KIAA0101_CSNK1G 0.87 0.971 0.036 0.003 MME 0.847 LASS4 0.669 0.906 0.003 0.003 MME 0.847 DYNLL1 0.787 0.919 0.043 0.006 MME 0.847 B4GALT3 0.785 0.926 0.009 0.014 MME 0.847 NP 0.787 0.912 0.03 0.021 MME 0.847 SPARC 0.661 0.929 0.043 0 MME 0.847 TNFRSF17 0.827 0.953 0.006 0.003 MME 0.847 PLEKHA3 0.777 0.919 0.02 0.017 MME 0.847 CMTM5 0.814 0.968 0.002 0.005 MME 0.847 IGLV6-57 0.757 0.92 0.047 0.002 MME 0.847 IGL@_IGLV1-44_(—) 0.77 0.919 0.006 0.013 MME 0.847 GLDC 0.775 0.9 0.008 0.033 MME 0.847 KIAA0101_CSNK1G 0.87 0.947 0.016 0.026 LASS4 0.669 RBP7 0.803 0.883 0.009 0.004 C7orf58 0.787 CAMK1D 0.735 0.881 0.004 0.022 DYNLL1 0.787 RBP7 0.803 0.921 0.003 0.02 DYNLL1 0.787 CAMK1D 0.735 0.887 0.005 0.021 DYNLL1 0.787 PPP1R2_PPP1R2P3 0.637 0.869 0.018 0.004 CPM 0.812 FAM118B 0.74 0.919 0.011 0.002 CPM 0.812 AIF1 0.745 0.897 0.04 0.009 FAM118B 0.74 CAMK1D 0.735 0.874 0.002 0.027 B4GALT3 0.785 RBP7 0.803 0.921 0.024 0.001 NP 0.787 RBP7 0.803 0.912 0.026 0.003 NP 0.787 MPZL2 0.775 0.879 0.039 0.013 POLE2 0.704 RPIA 0.655 0.854 0.001 0.017 POLE2 0.704 C1orf128 0.662 0.864 0.003 0.008 TNFRSF17 0.827 RGS2 0.789 0.913 0.007 0.039 PLEKHA3 0.777 RBP7 0.803 0.906 0.026 0.015 PLEKHA3 0.777 RGS2 0.789 0.913 0.005 0.02 PLEKHA3 0.777 CAMK1D 0.735 0.866 0.039 0.02 RBP7 0.803 CMTM5 0.814 0.976 0.001 0.001 RBP7 0.803 IGLV6-57 0.757 0.899 0.03 0.01 RBP7 0.803 IGL@_IGLV1-44_(—) 0.77 0.894 0.013 0.035 RBP7 0.803 SDHC 0.85 0.919 0.038 0.036 RBP7 0.803 GLDC 0.775 0.89 0.005 0.046 RBP7 0.803 KIAA0101_CSNK1G 0.87 0.958 0.004 0.01 RGS2 0.789 CMTM5 0.814 0.941 0.017 0.001 RGS2 0.789 CDC26 0.868 0.956 0.001 0.016 RGS2 0.789 IGL@_IGLV1-44_(—) 0.77 0.888 0.033 0.013 RGS2 0.789 KIAA0101_CSNK1G 0.87 0.932 0.032 0.008 CAMK1D 0.735 AIF1 0.745 0.876 0.011 0.009 CAMK1D 0.735 NFXL1 0.808 0.893 0.009 0.029 CAMK1D 0.735 DCTN5 0.773 0.885 0.004 0.022 CAMK1D 0.735 KIAA0101_CSNK1G 0.87 0.919 0.007 0.029 LOC100128751 0.809 DCTN5 0.773 0.899 0.033 0.031 IFI44 0.712 NA 0.623 0.83 0.007 0.01

TABLE 19 Ratios Mild ipSIRS Versus Severe ipSIRS Gene 1 Gene 2 Ratio Signif Ratio Signif Gene 1 Name AUC Gene 2 Name AUC Ratio AUC to Gene 1 to Gene 2 CD177 0.668 ACER3 0.649 0.815 0.046 0.049 CD177 0.668 NEK6_LOC1001290 0.471 0.748 0.043 0.008 CD177 0.668 GLT25D1 0.454 0.777 0.018 0.001 VNN1 0.649 UBE2J1 0.468 0.713 0.021 0.001 UBE2J1 0.468 SEC24A_SAR1B 0.679 0.795 0 0.04 IMP3 0.611 DNAJC9_FAM149B1 0.759 0.837 0.03 0.028 CLEC4D 0.522 FKBP5_LOC285847 0.423 0.654 0.035 0.027 GPR56 0.686 NA 0.629 0.797 0.045 0.016 C11orf82 0.619 DPH3 0.648 0.799 0.016 0.046 GNLY 0.691 GOT2 0.702 0.86 0.015 0.035 GNLY 0.691 HSPC159 0.612 0.789 0.018 0.047 TGFBR1 0.6 SEC24A_SAR1B 0.679 0.803 0.031 0.035 TGFBR1 0.6 MTRR 0.66 0.786 0.049 0.03 TGFBR1 0.6 KIAA0746 0.727 0.812 0.035 0.013 FKBP5_LOC285847 0.423 B3GNT5_MCF2L2 0.566 0.735 0.006 0.018 FKBP5_LOC285847 0.423 GLT25D1 0.454 0.698 0.042 0.03 C7orf53 0.75 DLEU2_DLEU2L 0.785 0.913 0.007 0.027 CAMK4 0.595 KPNA5 0.831 0.967 0 0.033 CAMK4 0.595 ITGA4_CERKL 0.659 0.842 0.002 0.034 GSR 0.795 VAMP2 0.811 0.92 0.004 0.042 GSR 0.795 JKAMP 0.752 0.94 0.005 0.004 GSR 0.795 SON 0.722 0.909 0.046 0.003 GSR 0.795 ATP6V0D1_LOC100 0.754 0.903 0.004 0.027 GSR 0.795 AIF1 0.826 0.918 0.034 0.03 KLRF1 0.702 MME 0.68 0.848 0.046 0.035 PICALM 0.595 DLEU2_DLEU2L 0.785 0.899 0.001 0.022 HIST1H4C 0.571 HIST1H3B 0.712 0.81 0.034 0.041 C9orf72 0.603 PPP2R5A_SNORA16 0.649 0.81 0.01 0.025 SLC15A2 0.629 EAF2_HCG11_LOC 0.682 0.844 0.012 0.025 ADM 0.649 CD63 0.645 0.818 0.022 0.025 LRRN3 0.724 EIF1AX_SCARNA9L 0.767 0.89 0.006 0.048 LRRN3 0.724 DPH3 0.648 0.845 0.039 0.008 LRRN3 0.724 E2F6 0.8 0.897 0.016 0.023 LRRN3 0.724 RFESD_SPATA9 0.815 0.895 0.014 0.043 HLA-DPB1 0.694 GOT2 0.702 0.859 0.032 0.017 VAMP2 0.811 SFRS9 0.841 0.97 0.003 0.018 JKAMP 0.752 LOC284757 0.834 0.916 0.031 0.011 JKAMP 0.752 SEC24A_SAR1B 0.679 0.898 0.038 0.002 JKAMP 0.752 SLC39A9 0.731 0.909 0.013 0.013 JKAMP 0.752 SFRS9 0.841 0.969 0 0.022 TCN1 0.755 AIG1 0.588 0.829 0.03 0.02 KPNA5 0.831 IRF4 0.759 0.941 0.041 0.017 CD63 0.645 SLC11A1 0.624 0.789 0.044 0.047 KLRK1_KLRC4 0.608 CCR4 0.44 0.758 0.011 0.012 ITK 0.646 ITGA4_CERKL 0.659 0.859 0.006 0.025 GOT2 0.702 HINT1 0.649 0.838 0.049 0.026 GOT2 0.702 ITGA4_CERKL 0.659 0.856 0.027 0.013 B3GAT3 0.605 EFCAB2 0.801 0.85 0.018 0.036 HIST1H4E 0.69 NF-E4 0.78 0.859 0.023 0.033 FGFBP2 0.698 PYHIN1 0.548 0.796 0.045 0.034 BPI 0.78 PPIF 0.683 0.833 0.033 0.05 BPI 0.78 AIG1 0.588 0.882 0.013 0.001 BPI 0.78 DPH3 0.648 0.865 0.036 0.012 BPI 0.78 S100B 0.703 0.906 0.011 0.008 BPI 0.78 CDC26 0.657 0.834 0.05 0.037 PPP2R5A_SNORA16 0.649 EAF2_HCG11_LOC 0.682 0.844 0.016 0.023 PPP2R5A_SNORA16 0.649 NA 0.682 0.818 0.048 0.018 PPP2R5A_SNORA16 0.649 EIF1AX_SCARNA9L 0.767 0.868 0.009 0.025 PPP2R5A_SNORA16 0.649 DPH3 0.648 0.812 0.047 0.014 PPP2R5A_SNORA16 0.649 TAF13 0.743 0.883 0.005 0.012 PPP2R5A_SNORA16 0.649 FSD1L_GARNL1 0.665 0.846 0.02 0.006 EAF2_HCG11_LOC 0.682 SEC24A_SAR1B 0.679 0.872 0.012 0.011 EAF2_HCG11_LOC 0.682 MTRR 0.66 0.845 0.046 0.007 EAF2_HCG11_LOC 0.682 KIAA0746 0.727 0.869 0.049 0.001 RCBTB2_LOC10013 0.578 NA 0.838 0.897 0.002 0.038 RCBTB2_LOC10013 0.578 PLEKHF2 0.85 0.924 0 0.044 SEC24A_SAR1B 0.679 TAF13 0.743 0.859 0.044 0.013 KLRD1 0.687 CCR4 0.44 0.799 0.029 0.003 KLRD1 0.687 PYHIN1 0.548 0.815 0.026 0.014 KLRD1 0.687 IGK@_IGKC_IGKV 0.664 0.818 0.018 0.037 SLC39A9 0.731 ZNF28 0.837 0.903 0.039 0.027 GIMAP7 0.618 DNAJC9_FAM149B1 0.759 0.89 0.003 0.013 HLA-DRA 0.669 HSP90AB1_HSP90A 0.735 0.865 0.006 0.047 DNAJC9_FAM149B1 0.759 HINT1 0.649 0.869 0.029 0.008 DNAJC9_FAM149B1 0.759 CENPK 0.781 0.922 0.003 0.045 DNAJC9_FAM149B1 0.759 ITGA4_CERKL 0.659 0.887 0.007 0.009 DNAJC9_FAM149B1 0.759 CTSL1_CTSLL3 0.697 0.856 0.03 0.028 NA 0.709 TRIM21 0.739 0.85 0.032 0.04 NA 0.709 NA 0.838 0.917 0.01 0.04 MTRR 0.66 TAF13 0.743 0.876 0.014 0.01 MTRR 0.66 FSD1L_GARNL1 0.665 0.859 0.014 0.007 MTRR 0.66 PLEKHF2 0.85 0.916 0.004 0.047 HIST1H3A 0.593 TYMS 0.761 0.899 0.001 0.015 HIST1H3A 0.593 HIST1H2BM 0.693 0.875 0.001 0.011 HIST1H3A 0.593 HIST1H3B 0.712 0.891 0.005 0.001 HIST1H3A 0.593 IGK@_IGKC_IGKV 0.664 0.777 0.04 0.046 TREML1 0.581 MYL9 0.472 0.717 0.028 0.006 TREML1 0.581 CMTMS 0.494 0.679 0.039 0.03 PDE3B 0.622 EIF1AX_SCARNA9L 0.767 0.844 0.025 0.026 PDE3B 0.622 TAF13 0.743 0.844 0.01 0.037 PDE3B 0.622 DLEU2_DLEU2L 0.785 0.867 0.005 0.043 PDE3B 0.622 E2F6 0.8 0.856 0.025 0.037 PDE3B 0.622 ATP5L_ATP5L2 0.797 0.878 0.006 0.033 PDE3B 0.622 CALM2_C2orf61 0.776 0.905 0.001 0.024 CENPK 0.781 IRF4 0.759 0.95 0.003 0.02 AMFR 0.582 E2F6 0.8 0.857 0.003 0.042 HSP90AB1_HSP90A 0.735 RFESD_SPATA9 0.815 0.887 0.026 0.041 HSP90AB1_HSP90A 0.735 SON 0.722 0.865 0.005 0.049 EIF1AX_SCARNA9L 0.767 CEP97 0.702 0.91 0.005 0.007 SFRS9 0.841 PLEKHA3 0.875 0.974 0.018 0.014 DPH3 0.648 KIAA0746 0.727 0.852 0.01 0.04 NF-E4 0.78 E2F6 0.8 0.906 0.022 0.041 NPCDR1 0.752 KLHL5 0.739 0.861 0.043 0.044 NPCDR1 0.752 SON 0.722 0.861 0.032 0.047 TAF13 0.743 CEP97 0.702 0.852 0.041 0.049 KIAA0746 0.727 FSD1L_GARNL1 0.665 0.848 0.046 0.017 ABCG1 0.669 RFESD_SPATA9 0.815 0.879 0.016 0.046 FAM118A 0.644 E2F6 0.8 0.876 0.017 0.02 E2F6 0.8 RASA4_RASA4P_R 0.645 0.861 0.031 0.023 S100B 0.703 MPO 0.705 0.856 0.029 0.018 CEP97 0.702 CALM2_C2orf61 0.776 0.895 0.003 0.043

TABLE 20 Ratios Mild ipSIRS Versus Shock ipSIRS Gene 1 Name Gene 1 AUC Gene 2 Name Gene 2 AUC Ratio AUC Ratio Signif to Gene 1 Ratio Signif to Gene 2 TLR5 0.769 NA 0.808 0.893 0.021 0.05 TLR5 0.769 DLEU2_DLEU2L 0.782 0.874 0.03 0.029 TLR5 0.769 NA 0.838 0.908 0.022 0.012 TLR5 0.769 ATP6V0D1_LOC100 0.701 0.859 0.018 0.017 VNN1 0.71 HIST1H4E 0.678 0.826 0.01 0.017 VNN1 0.71 SLC11A1 0.556 0.768 0.047 0.023 VNN1 0.71 CENPK 0.752 0.851 0.017 0.021 VNN1 0.71 ERGIC1 0.6 0.769 0.044 0.044 VNN1 0.71 SON 0.632 0.781 0.044 0.047 VNN1 0.71 PLEKHF2 0.692 0.864 0.002 0.011 VNN1 0.71 CALM2_C2orf61 0.687 0.848 0.003 0.016 UBE2J1 0.645 PLEKHF2 0.692 0.81 0.046 0.001 IMP3 0.742 GSR 0.723 0.864 0.027 0.013 IMP3 0.742 SLC39A9 0.736 0.873 0.012 0.025 ARG1 0.683 HMGB2 0.556 0.749 0.034 0.002 FAR2 0.746 VAMP2 0.834 0.923 0 0.046 FAR2 0.746 TRIM21 0.779 0.869 0.035 0.023 FAR2 0.746 ZNF28 0.797 0.883 0.018 0.022 FAR2 0.746 PPP1R2_PPP1R2P3 0.739 0.857 0.03 0.021 FAR2 0.746 ATP5L_ATP5L2 0.786 0.904 0.007 0.006 GNLY 0.792 GSR 0.723 0.851 0.05 0.041 OMG 0.709 C9orf72 0.573 0.784 0.028 0.022 OMG 0.709 JKAMP 0.554 0.793 0.023 0.007 OMG 0.709 HIST1H4E 0.678 0.801 0.047 0.037 OMG 0.709 CDS2 0.562 0.776 0.01 0.02 OMG 0.709 PLEKHF2 0.692 0.836 0.011 0.025 OMG 0.709 AIF1 0.719 0.83 0.021 0.031 OMG 0.709 CALM2_C2orf61 0.687 0.824 0.014 0.037 SLC37A3 0.653 HIST1H4E 0.678 0.802 0.024 0.02 BMX_HNRPDL 0.749 PLEKHF2 0.692 0.833 0.018 0.046 BMX_HNRPDL 0.749 CALM2_C2orf61 0.687 0.822 0.029 0.05 STOM 0.684 VAMP2 0.834 0.923 0 0.022 STOM 0.684 MINPP1 0.78 0.848 0.024 0.016 STOM 0.684 ATP5L_ATP5L2 0.786 0.865 0.005 0.034 TDRD9 0.735 AGTRAP 0.569 0.768 0.046 0.044 FAIM3 0.749 SLC15A2 0.733 0.861 0.01 0.026 FAIM3 0.749 SRXN1 0.7 0.834 0.015 0.049 CLEC4E 0.681 HIST1H4E 0.678 0.8 0.047 0.021 CLEC4E 0.681 ERGIC1 0.6 0.792 0.007 0.027 CLEC4E 0.681 PLEKHF2 0.692 0.819 0.011 0.038 CLEC4E 0.681 CALM2_C2orf61 0.687 0.823 0.007 0.033 IL18R1 0.715 SLC11A1 0.556 0.763 0.025 0.034 IL18R1 0.715 RRP12_LOC644215 0.569 0.76 0.014 0.035 TGFBR1 0.663 NA 0.838 0.909 0.001 0.024 FKBP5_LOC285847 0.685 HIST1H4E 0.678 0.804 0.022 0.016 FKBP5_LOC285847 0.685 PLEKHF2 0.692 0.823 0.008 0.024 FKBP5_LOC285847 0.685 CALM2_C2orf61 0.687 0.815 0.012 0.029 PLB1 0.651 EFCAB2 0.772 0.851 0.01 0.022 DSE 0.623 PLEKHF2 0.692 0.827 0.006 0.01 DSE 0.623 ARL17P1_ARL17 0.745 0.805 0.024 0.041 CAMK4 0.726 HDHD1A 0.647 0.804 0.048 0.035 DNAJC13 0.661 ARL17P1_ARL17 0.745 0.819 0.019 0.048 GSR 0.723 MINPP1 0.78 0.845 0.034 0.026 GSR 0.723 TRIM21 0.779 0.866 0.013 0.035 GSR 0.723 ZNF28 0.797 0.887 0.005 0.024 GSR 0.723 LY6G5B_CSNK2B 0.737 0.897 0.003 0.003 GSR 0.723 ZNF587_ZNF417 0.842 0.927 0.001 0.025 GSR 0.723 KDM6B_TMEM88 0.695 0.827 0.028 0.038 GSR 0.723 PLEKHF2 0.692 0.833 0.049 0.002 GSR 0.723 ATP6V0D1_LOC100 0.701 0.85 0.002 0.042 GSR 0.723 PPP1R2_PPP1R2P3 0.739 0.84 0.026 0.044 GSR 0.723 ATP5L_ATP5L2 0.786 0.878 0.004 0.035 SGMS2 0.752 ERGIC1 0.6 0.838 0.042 0.004 SGMS2 0.752 ATP5L_ATP5L2 0.786 0.878 0.005 0.046 B3GNT5_MCF2L2 0.692 EFCAB2 0.772 0.898 0.005 0.003 B3GNT5_MCF2L2 0.692 PLEKHF2 0.692 0.838 0.008 0.038 B3GNT5_MCF2L2 0.692 CALM2_C2orf61 0.687 0.829 0.012 0.033 GK3P_GK 0.729 DPH3 0.536 0.806 0.048 0.003 GK3P_GK 0.729 CALM2_C2orf61 0.687 0.842 0.009 0.038 PICALM 0.749 GIMAP7 0.827 0.888 0.019 0.04 PICALM 0.749 NA 0.808 0.887 0.012 0.035 PICALM 0.749 DLEU2_DLEU2L 0.782 0.911 0.009 0.002 HIST1H4C 0.638 ANAPC11 0.58 0.744 0.031 0.03 SLC15A2 0.733 ITK 0.736 0.856 0.049 0.004 SLC15A2 0.733 TRIM21 0.779 0.864 0.032 0.047 SLC15A2 0.733 PPP1R2_PPP1R2P3 0.739 0.86 0.012 0.025 SLC15A2 0.733 ATP5L_ATP5L2 0.786 0.873 0.014 0.029 VAMP2 0.834 CD63 0.722 0.945 0.026 0 VAMP2 0.834 SLC39A9 0.736 0.931 0.025 0 VAMP2 0.834 AREG 0.668 0.909 0.045 0 VAMP2 0.834 SFRS9 0.798 0.933 0.047 0.005 VAMP2 0.834 SRXN1 0.7 0.922 0.023 0 VAMP2 0.834 C4orf3 0.776 0.962 0.001 0.001 SMPDL3A 0.733 EFCAB2 0.772 0.883 0.008 0.042 SMPDL3A 0.733 TAF13 0.585 0.834 0.04 0.003 SMPDL3A 0.733 SON 0.632 0.809 0.007 0.04 SMPDL3A 0.733 TMEM62_SPCS2_L 0.629 0.81 0.035 0.024 SMPDL3A 0.733 PLEKHF2 0.692 0.857 0.014 0.021 JKAMP 0.554 PTGER2 0.616 0.731 0.047 0.032 SLC1A3 0.807 EFCAB2 0.772 0.902 0.046 0.008 SLC1A3 0.807 PPP1R2_PPP1R2P3 0.739 0.869 0.04 0.026 TCN1 0.726 FGFBP2 0.696 0.809 0.04 0.049 TCN1 0.726 PMS2CL_PMS2 0.581 0.79 0.024 0.006 TCN1 0.726 MYL9 0.6 0.777 0.05 0.033 ODZ1 0.733 HIST1H4E 0.678 0.825 0.02 0.025 KPNA5 0.845 SLC39A9 0.736 0.898 0.03 0.012 KPNA5 0.845 SFRS9 0.798 0.917 0.003 0.044 KPNA5 0.845 C4orf3 0.776 0.947 0.006 0.002 CD63 0.722 AGTRAP 0.569 0.82 0.025 0.004 CD63 0.722 TRIM21 0.779 0.863 0.022 0.044 CD63 0.722 ZNF28 0.797 0.873 0.007 0.034 CD63 0.722 LY6G5B_CSNK2B 0.737 0.856 0.015 0.048 CD63 0.722 NA 0.838 0.917 0.002 0.029 CD63 0.722 KDM6B_TMEM88 0.695 0.851 0.01 0.014 DDAH2 0.681 HIST1H4E 0.678 0.832 0.016 0.012 DDAH2 0.681 EFCAB2 0.772 0.851 0.028 0.017 DDAH2 0.681 AGTRAP 0.569 0.793 0.008 0.023 DDAH2 0.681 KDM6B_TMEM88 0.695 0.833 0.003 0.05 DDAH2 0.681 PLEKHF2 0.692 0.837 0.018 0.01 DDAH2 0.681 AIF1 0.719 0.822 0.021 0.044 ATP13A3 0.664 PLEKHF2 0.692 0.833 0.004 0.021 ITK 0.736 HDHD1A 0.647 0.812 0.037 0.022 ITK 0.736 TSHZ2 0.529 0.842 0.037 0 HIST1H4E 0.678 PPP2R5A_SNORA16 0.673 0.811 0.017 0.036 HIST1H4E 0.678 FOLR3_FOLR2 0.691 0.806 0.044 0.013 HIST1H4E 0.678 LGALS1 0.643 0.816 0.005 0.03 HIST1H4E 0.678 MTHFS 0.649 0.788 0.036 0.043 HIST1H4E 0.678 AP3B2 0.722 0.822 0.027 0.026 HIST1H4E 0.678 GSTO1 0.604 0.811 0.043 0.003 HIST1H4E 0.678 REIN 0.756 0.857 0.008 0.046 HIST1H4E 0.678 CD151 0.582 0.786 0.047 0.004 HIST1H4E 0.678 C4orf3 0.776 0.905 0 0.048 HIST1H4E 0.678 CDC26 0.519 0.792 0.018 0.001 FGFBP2 0.696 MKI67 0.667 0.793 0.032 0.045 ECHDC3 0.702 PLEKHF2 0.692 0.818 0.046 0.025 ECHDC3 0.702 CALM2_C2orf61 0.687 0.834 0.023 0.017 HSPC159 0.612 MINPP1 0.78 0.842 0.002 0.026 OLAH 0.723 DPH3 0.536 0.767 0.046 0.015 PPP2R5A_SNORA16 0.673 CENPK 0.752 0.851 0.025 0.004 PPP2R5A_SNORA16 0.673 MINPP1 0.78 0.846 0.014 0.03 PPP2R5A_SNORA16 0.673 PLEKHF2 0.692 0.849 0.012 0.003 PPP2R5A_SNORA16 0.673 CALM2_C2orf61 0.687 0.845 0.011 0.005 SEC24A_SAR1B 0.716 PLEKHF2 0.692 0.834 0.04 0.006 SLC39A9 0.736 NA 0.808 0.882 0.019 0.044 SLC39A9 0.736 ZNF28 0.797 0.896 0.019 0.003 SLC39A9 0.736 NA 0.838 0.91 0.006 0.016 SLC39A9 0.736 ZNF587_ZNF417 0.842 0.933 0.003 0.01 SLC39A9 0.736 ATP6V0D1_LOC100 0.701 0.846 0.017 0.031 SLC39A9 0.736 ATP5L_ATP5L2 0.786 0.882 0.03 0.006 EXOSC4 0.723 EFCAB2 0.772 0.856 0.043 0.04 EXOSC4 0.723 AGTRAP 0.569 0.791 0.035 0.023 EXOSC4 0.723 PLEKHF2 0.692 0.849 0.03 0.008 NA 0.808 C4orf3 0.776 0.899 0.026 0.029 HIST1H4L 0.596 CD24 0.721 0.807 0.01 0.032 LGALS1 0.643 CENPK 0.752 0.811 0.044 0.032 LGALS1 0.643 ZNF28 0.797 0.875 0.002 0.022 LGALS1 0.643 AIF1 0.719 0.844 0.012 0.002 PPIF 0.704 SRXN1 0.7 0.813 0.033 0.048 MTHFS 0.649 EFCAB2 0.772 0.825 0.034 0.047 MTHFS 0.649 PLEKHF2 0.692 0.813 0.026 0.025 MTHFS 0.649 CALM2_C2orf61 0.687 0.816 0.015 0.017 LCN2 0.679 PMS2CL_PMS2 0.581 0.745 0.032 0.047 AP3B2 0.722 AGTRAP 0.569 0.788 0.035 0.019 AP3B2 0.722 RRP12_LOC644215 0.569 0.781 0.047 0.023 AP3B2 0.722 AIF1 0.719 0.843 0.013 0.04 EFCAB2 0.772 REIN 0.756 0.867 0.029 0.044 EFCAB2 0.772 GALNT2 0.674 0.848 0.023 0.017 HIST1H2AA 0.769 DLEU2_DLEU2L 0.782 0.908 0.013 0.002 HIST1H3A 0.642 CD24 0.721 0.82 0.028 0.01 C22orf37 0.725 C4orf3 0.776 0.864 0.027 0.042 SLC39A8 0.646 PLEKHF2 0.692 0.832 0.005 0.022 SLC39A8 0.646 CALM2_C2orf61 0.687 0.794 0.019 0.047 AREG 0.668 CENPK 0.752 0.815 0.05 0.042 PTGER2 0.616 PLEKHF2 0.692 0.808 0.005 0.044 PMS2CL_PMS2 0.581 FSD1L_GARNL1 0.595 0.728 0.048 0.025 RETN 0.756 KDM6B_TMEM88 0.695 0.852 0.026 0.022 RETN 0.756 AIF1 0.719 0.856 0.026 0.019 PDE3B 0.582 CENPK 0.752 0.82 0.006 0.025 PDE3B 0.582 CALM2_C2orf61 0.687 0.81 0.003 0.041 CENPK 0.752 HDHD1A 0.647 0.816 0.037 0.036 CD24 0.721 HIST1H3C 0.558 0.82 0.036 0.001 CD24 0.721 MYL9 0.6 0.777 0.05 0.042 HDHD1A 0.647 MGST3 0.647 0.802 0.016 0.023 LRRFIP1 0.744 ZNF587_ZNF417 0.842 0.926 0.002 0.049 LRRFIP1 0.744 PPP1R2_PPP1R2P3 0.739 0.869 0.015 0.022 LRRFIP1 0.744 CALM2_C2orf61 0.687 0.881 0.011 0.001 SFRS9 0.798 NA 0.838 0.92 0.028 0.007 SFRS9 0.798 ZNF587_ZNF417 0.842 0.93 0.034 0.009 SFRS9 0.798 PLEKHA3 0.794 0.896 0.047 0.03 MINPP1 0.78 C4orf3 0.776 0.914 0.001 0.033 ZNF28 0.797 C4orf3 0.776 0.915 0.005 0.011 TAF13 0.585 FSD1L_GARNL1 0.595 0.776 0.016 0.012 THBS1 0.619 POLE2 0.708 0.773 0.026 0.039 HIST1H3B 0.488 HIST1H3C 0.558 0.732 0.008 0.015 ZRANB1 0.521 RFESD_SPATA9 0.697 0.761 0.003 0.043 FSD1L_GARNL1 0.595 RFESD_SPATA9 0.697 0.793 0.011 0.047 NA 0.838 C4orf3 0.776 0.915 0.012 0.013 UBE2F_C20orf194 0.622 POLE2 0.708 0.804 0.039 0.009 ZNF587_ZNF417 0.842 C4orf3 0.776 0.916 0.038 0.018 C4orf3 0.776 PLEKHA3 0.794 0.924 0.003 0.009 C4orf3 0.776 PLEKHF2 0.692 0.911 0.029 0 C4orf3 0.776 ATP6V0D1_LOC100 0.701 0.854 0.049 0.032 C4orf3 0.776 ATP5L_ATP5L2 0.786 0.905 0.019 0.004

TABLE 21 Ratios Severe ipSIRS Versus Shock ipSIRS Gene 1 Name Gene 1 AUC Gene 2 Name Gene 2 AUC Ratio AUC Ratio Signif to Gene 1 Ratio Signif to Gene 2 ACER3 0.724 GSR 0.554 0.804 0.014 0.027 ACER3 0.724 MTRR 0.552 0.842 0.017 0.001 CAMK4 0.777 PTGER2 0.653 0.841 0.036 0.014 GSR 0.554 SH3PXD2B 0.676 0.77 0.013 0.032 PICALM 0.612 SYNE2 0.678 0.799 0.029 0.05 SLC15A2 0.619 CCR4 0.693 0.797 0.041 0.038 TMEM144_LOC2855 0.578 PLIN2 0.434 0.652 0.028 0.048 MKI67 0.671 RETN 0.664 0.82 0.022 0.047 MME 0.671 CFD 0.554 0.767 0.022 0.036

Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers.

Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications, which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described. 

The claims defining the invention are as follows:
 1. A method for treating or inhibiting the development of infection-negative systemic inflammatory response syndrome (inSIRS) or infection-positive systemic inflammatory response syndrome (ipSIRS) in a subject, the method comprising: (1) providing a reference inflammatory response syndrome (IRS) biomarker profile that correlates presence of inSIRS with a first level of a PLAC8 expression product in a reference peripheral blood sample, and that correlates presence of ipSIRS with a second level of the PLAC8 expression product the reference peripheral blood sample, wherein the PLAC8 expression product is an IRS biomarker; (2) obtaining a sample IRS biomarker profile that evaluates the level of the PLAC8 expression product in a peripheral blood sample taken from the subject; (3) determining an increased likelihood of the presence of inSIRS or the presence of ipSIRS in the subject based on whether the sample IRS biomarker profile comprises the first level or the second level of the PLAC8 expression product; and (4) administering to the subject an effective amount of an anti-inflammatory or anti-pyretic agent if the subject is determined to have an increased likelihood of having inSIRS, or administering to the subject an effective amount of an antibiotic agent if the subject is determined to have an increased likelihood of having ipSIRS.
 2. The method according to claim 1, wherein the reference IRS biomarker profile correlates presence of inSIRS with a first level of another IRS biomarker and correlates presence of ipSIRS with a second level of the other IRS biomarker, wherein the sample IRS biomarker profile evaluates the level of the other IRS biomarker in the sample, and wherein an increased likelihood of the presence of inSIRS or the presence of ipSIRS in the subject is determined based on whether the sample IRS biomarker profile comprises the first level or the second level of the other IRS biomarker, wherein the other IRS biomarker is an expression product of a gene selected from the group consisting of C11orf82, 132, INSIG1, CDS2, VOPP1, SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10, PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2, SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41, TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1, NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM, MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2, HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1, KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1, FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1, KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDC26, ICAM1, LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B, C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL, SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7, GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125, SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ, FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO, TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B, IFI16, CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3, ANKRD28, TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B and LASS4.
 3. The method according to claim 2, comprising comparing the level of the PLAC8 expression product with the level of the other IRS biomarker in the sample IRS biomarker profile to provide a ratio and determining a likelihood of the presence or absence of inSIRS or ipSIRS based on that ratio.
 4. The method according to claim 2, wherein the other IRS biomarker is a PLA2G7 expression product. 